Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00041
S. Nageshrao, Bruno Costa, Dimitar Filev
In many industrial applications, one of the major bottlenecks in using advanced learning-based methods (such as reinforcement learning) for controls is the lack of interpretability of the trained agent. In this paper, we present a methodology for translating a trained reinforcement learning agent into a set of simple and easy to interpret if-then rules by using the proven universal approximation property of the rules with fuzzy predicates. Proposed methodology combines the optimality of reinforcement learning with interpretability of the theory of approximate reasoning, thus making reinforcement learning-based solutions more accessible to industrial practitioners. The framework presented in this paper has the potential to help address the fundamental problem in widespread adoption of reinforcement learning in industrial applications.
{"title":"Interpretable Approximation of a Deep Reinforcement Learning Agent as a Set of If-Then Rules","authors":"S. Nageshrao, Bruno Costa, Dimitar Filev","doi":"10.1109/ICMLA.2019.00041","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00041","url":null,"abstract":"In many industrial applications, one of the major bottlenecks in using advanced learning-based methods (such as reinforcement learning) for controls is the lack of interpretability of the trained agent. In this paper, we present a methodology for translating a trained reinforcement learning agent into a set of simple and easy to interpret if-then rules by using the proven universal approximation property of the rules with fuzzy predicates. Proposed methodology combines the optimality of reinforcement learning with interpretability of the theory of approximate reasoning, thus making reinforcement learning-based solutions more accessible to industrial practitioners. The framework presented in this paper has the potential to help address the fundamental problem in widespread adoption of reinforcement learning in industrial applications.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134009517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00316
M. Boger, A. Laverghetta, Nikolai Fetisov, John Licato
Analogical reasoning, it has been argued, fundamentally underlies many cognitive processes and is an important marker of developmental cognition. This connection suggests that the clever use of analogical reasoning tasks can improve cognitive performance in specific ways, thus leading to clear educational applications, as recent psychological work has confirmed. However, currently there are no known methods to either solve or generate analogical word problems, at least to a degree of reliability that would be necessary before such educational applications are possible. To address these concerns we present work to both solve and generate analogy word problems: First, given an analogy word problem, our algorithm performs a parallel random walk through the semantic network ConceptNet to limit the number of choices that are then considered by a vector embedding. We achieve an improvement in accuracy beyond existing state-of-the-art. Second, we explore a method for automatically generating explainable n-step analogy word problems, and analyze the results.
{"title":"Generating Near and Far Analogies for Educational Applications: Progress and Challenges","authors":"M. Boger, A. Laverghetta, Nikolai Fetisov, John Licato","doi":"10.1109/ICMLA.2019.00316","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00316","url":null,"abstract":"Analogical reasoning, it has been argued, fundamentally underlies many cognitive processes and is an important marker of developmental cognition. This connection suggests that the clever use of analogical reasoning tasks can improve cognitive performance in specific ways, thus leading to clear educational applications, as recent psychological work has confirmed. However, currently there are no known methods to either solve or generate analogical word problems, at least to a degree of reliability that would be necessary before such educational applications are possible. To address these concerns we present work to both solve and generate analogy word problems: First, given an analogy word problem, our algorithm performs a parallel random walk through the semantic network ConceptNet to limit the number of choices that are then considered by a vector embedding. We achieve an improvement in accuracy beyond existing state-of-the-art. Second, we explore a method for automatically generating explainable n-step analogy word problems, and analyze the results.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133529007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00272
Marc Wenninger, Dominik Stecher, Jochen Schmidt
Generating a more detailed understanding of domestic electricity demand is a major topic for energy suppliers and householders in times of climate change. Over the years there have been many studies on consumption feedback systems to inform householders, disaggregation algorithms for Non-Intrusive-Load-Monitoring (NILM), Real-Time-Pricing (RTP) to promote supply aware behavior through monetary incentives and appliance usage prediction algorithms. While these studies are vital steps towards energy awareness, one of the most fundamental challenges has not yet been tackled: Automated detection of start and stop of usage cycles of household appliances. We argue that most research efforts in this area will benefit from a reliable segmentation method to provide accurate usage information. We propose a SVM-based segmentation method for home appliances such as dishwashers and washing machines. The method is evaluated using manually annotated electricity measurements of five different appliances recorded over two years in multiple households.
{"title":"SVM-Based Segmentation of Home Appliance Energy Measurements","authors":"Marc Wenninger, Dominik Stecher, Jochen Schmidt","doi":"10.1109/ICMLA.2019.00272","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00272","url":null,"abstract":"Generating a more detailed understanding of domestic electricity demand is a major topic for energy suppliers and householders in times of climate change. Over the years there have been many studies on consumption feedback systems to inform householders, disaggregation algorithms for Non-Intrusive-Load-Monitoring (NILM), Real-Time-Pricing (RTP) to promote supply aware behavior through monetary incentives and appliance usage prediction algorithms. While these studies are vital steps towards energy awareness, one of the most fundamental challenges has not yet been tackled: Automated detection of start and stop of usage cycles of household appliances. We argue that most research efforts in this area will benefit from a reliable segmentation method to provide accurate usage information. We propose a SVM-based segmentation method for home appliances such as dishwashers and washing machines. The method is evaluated using manually annotated electricity measurements of five different appliances recorded over two years in multiple households.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130279786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00019
Dan Halbersberg, B. Lerner
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease, lasting from the day of onset until death. Factors such as the progression rate and pattern of the disease vary greatly among patients, making it difficult to achieve accurate predictions about ALS. To accurately predict ALS disease state and deterioration, we propose a novel approach that combines: a) sequence clustering based on dynamic time warping for separation among patients with diverse ALS deterioration patterns, b) sequential pattern mining for discovery of deterioration changes that patients of the same type may have in common, and c) deterioration-based patient next-state prediction. Using a clinical dataset, we demonstrate the advantage of the proposed approach in terms of classification accuracy and deterioration detection compared to other classification methods and temporal models such as long short-term memory.
{"title":"Temporal Modeling of Deterioration Patterns and Clustering for Disease Prediction of ALS Patients","authors":"Dan Halbersberg, B. Lerner","doi":"10.1109/ICMLA.2019.00019","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00019","url":null,"abstract":"Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease, lasting from the day of onset until death. Factors such as the progression rate and pattern of the disease vary greatly among patients, making it difficult to achieve accurate predictions about ALS. To accurately predict ALS disease state and deterioration, we propose a novel approach that combines: a) sequence clustering based on dynamic time warping for separation among patients with diverse ALS deterioration patterns, b) sequential pattern mining for discovery of deterioration changes that patients of the same type may have in common, and c) deterioration-based patient next-state prediction. Using a clinical dataset, we demonstrate the advantage of the proposed approach in terms of classification accuracy and deterioration detection compared to other classification methods and temporal models such as long short-term memory.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130330356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00163
Mohammed Bany Muhammad, A. Moinuddin, M. Lee, Yanfei Zhang, V. Abedi, R. Zand, M. Yeasin
The assessment of knee joint gap and severity of Osteoarthritis (OA) is subjective and often inaccurate. The main source of error is due to the judgement of human expert from low resolution images (i.e., X-ray images). To address the problem, we developed an ensemble of Deep Learning (DL) model to objectively score the severity of OA only from the radiometric images. The proposed method consists of two main modules. First, we developed a scale invariant and aspect ratio preserving automatic localization and characterization of the kneecap area. Second, we developed multiple instances of "hyper parameter optimized" DL models and fused them using ensemble classification to score the severity of OA. In this implementation, we used three convolutional neural networks to improve the bias-variance trade-off, and boost accuracy and generalization. We tested our modeling framework using a collection of 4,796 X-ray images from Osteoarthritis Initiative (OAI). Our results show a higher performance (~ 2-8%) when compared to the state-of-the-art methods. Finally, this machine learning-based methodology provides a pipeline in decision support system for assessing and quantifying the OA severity.
{"title":"Deep Ensemble Network for Quantification and Severity Assessment of Knee Osteoarthritis","authors":"Mohammed Bany Muhammad, A. Moinuddin, M. Lee, Yanfei Zhang, V. Abedi, R. Zand, M. Yeasin","doi":"10.1109/ICMLA.2019.00163","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00163","url":null,"abstract":"The assessment of knee joint gap and severity of Osteoarthritis (OA) is subjective and often inaccurate. The main source of error is due to the judgement of human expert from low resolution images (i.e., X-ray images). To address the problem, we developed an ensemble of Deep Learning (DL) model to objectively score the severity of OA only from the radiometric images. The proposed method consists of two main modules. First, we developed a scale invariant and aspect ratio preserving automatic localization and characterization of the kneecap area. Second, we developed multiple instances of \"hyper parameter optimized\" DL models and fused them using ensemble classification to score the severity of OA. In this implementation, we used three convolutional neural networks to improve the bias-variance trade-off, and boost accuracy and generalization. We tested our modeling framework using a collection of 4,796 X-ray images from Osteoarthritis Initiative (OAI). Our results show a higher performance (~ 2-8%) when compared to the state-of-the-art methods. Finally, this machine learning-based methodology provides a pipeline in decision support system for assessing and quantifying the OA severity.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115078464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00169
Okwudili M. Ezeme, Q. Mahmoud, Akramul Azim
One of the multiplier effects of the boom in mobile technologies ranging from cell phones to computers and wearables like smart watches is that every public and private common spaces are now dotted with Wi-Fi hotspots. These hotspots provide the convenience of accessing the internet on-the-go for either play or work. Also, with the increased automation of our daily routines by our mobile devices via a multitude of applications, our vulnerability to cyber fraud or attacks becomes higher too. Hence, the need for heightened security that is capable of detecting anomalies on-the-fly. However, these edge devices connected to the local area network come with diverse capabilities with varying degrees of limitations in compute and energy resources. Therefore, running a process-based anomaly detector is not given a high priority in these devices because; a) the primary functions of the applications running on the devices is not security; therefore, the device allocates much of its resources into satisfying the primary duty of the applications. b) the volume and velocity of the data are high. Therefore, in this paper, we introduce a multi-node (nodes and devices are used interchangeably in the paper) ad-hoc network that uses a novel offloading scheme to bring an online anomaly detection capability on the kernel events to the nodes in the network. We test the framework in a Wi-Fi-based ad-hoc network made up of several devices, and the results confirm our hypothesis that the scheme can reduce latency and increase the throughput of the anomaly detector, thereby making online anomaly detection in the edge possible without sacrificing the accuracy of the deep recurrent neural network.
{"title":"A Deep Learning Approach to Distributed Anomaly Detection for Edge Computing","authors":"Okwudili M. Ezeme, Q. Mahmoud, Akramul Azim","doi":"10.1109/ICMLA.2019.00169","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00169","url":null,"abstract":"One of the multiplier effects of the boom in mobile technologies ranging from cell phones to computers and wearables like smart watches is that every public and private common spaces are now dotted with Wi-Fi hotspots. These hotspots provide the convenience of accessing the internet on-the-go for either play or work. Also, with the increased automation of our daily routines by our mobile devices via a multitude of applications, our vulnerability to cyber fraud or attacks becomes higher too. Hence, the need for heightened security that is capable of detecting anomalies on-the-fly. However, these edge devices connected to the local area network come with diverse capabilities with varying degrees of limitations in compute and energy resources. Therefore, running a process-based anomaly detector is not given a high priority in these devices because; a) the primary functions of the applications running on the devices is not security; therefore, the device allocates much of its resources into satisfying the primary duty of the applications. b) the volume and velocity of the data are high. Therefore, in this paper, we introduce a multi-node (nodes and devices are used interchangeably in the paper) ad-hoc network that uses a novel offloading scheme to bring an online anomaly detection capability on the kernel events to the nodes in the network. We test the framework in a Wi-Fi-based ad-hoc network made up of several devices, and the results confirm our hypothesis that the scheme can reduce latency and increase the throughput of the anomaly detector, thereby making online anomaly detection in the edge possible without sacrificing the accuracy of the deep recurrent neural network.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115715692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00275
Nikola Marković, T. Stoetzel, V. Staudt, D. Kolossa
This paper proposes a novel approach for condition monitoring of power electronic systems. When monitoring the state of a power system, reliability is crucial, as this type of system is usually operated continuously for long periods of time, and as both missed faults as well as false detections can easily become prohibitively expensive. Recently, machine-learning-based methods for fault detection of power systems have gained popularity, since they can overcome many of the constrains of model-based techniques. Most of these methods train classifiers for different states of the system under test, and thus, the problem of fault detection becomes a problem of classification. In this paper we compare two of such recent techniques. We show that despite good results, it cannot reasonably be expected that the state classification is solved perfectly for every instant of time, which makes the application of such classifiers infeasible in practical systems. In order to overcome these issues, we propose to re-formulate the task into one of hybrid—neural and statistical—cross-temporal hypothesis testing. This novel hybrid framework allows us to build upon the previous machine-learning-based classification approaches, and to achieve full reliability on a challenging dataset of fault monitoring measurements for a buck-converter.
{"title":"Hybrid Condition Monitoring for Power Electronic Systems","authors":"Nikola Marković, T. Stoetzel, V. Staudt, D. Kolossa","doi":"10.1109/ICMLA.2019.00275","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00275","url":null,"abstract":"This paper proposes a novel approach for condition monitoring of power electronic systems. When monitoring the state of a power system, reliability is crucial, as this type of system is usually operated continuously for long periods of time, and as both missed faults as well as false detections can easily become prohibitively expensive. Recently, machine-learning-based methods for fault detection of power systems have gained popularity, since they can overcome many of the constrains of model-based techniques. Most of these methods train classifiers for different states of the system under test, and thus, the problem of fault detection becomes a problem of classification. In this paper we compare two of such recent techniques. We show that despite good results, it cannot reasonably be expected that the state classification is solved perfectly for every instant of time, which makes the application of such classifiers infeasible in practical systems. In order to overcome these issues, we propose to re-formulate the task into one of hybrid—neural and statistical—cross-temporal hypothesis testing. This novel hybrid framework allows us to build upon the previous machine-learning-based classification approaches, and to achieve full reliability on a challenging dataset of fault monitoring measurements for a buck-converter.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114160327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00307
Yo Ehara
Assessing whether an ungraded second language learner can read a given text quickly is important for further instructing and supporting the learner, particularly when evaluating numerous ungraded learners from diverse backgrounds. Second language acquisition (SLA) studies have tackled such assessment tasks wherein only a single short vocabulary test result is available to assess a learner; such studies have shown that the text-coverage, i.e., the percentage of words the learner knows in the text, is the key assessment measure. Currently, count-based percentages are used, in which each word in the given text is classified as being known or unknown to the learner, and the words classified as known are then simply counted. When each word is classified, we can also obtain an uncertainty value as to how likely each word is known to the learner. Although such values can be informative for a readability assessment, how to leverage these values to guarantee their use as an assessment measure that is comparable to that of the previous values remains unclear. We propose a novel framework that allows assessment methods to be uncertainty-aware while guaranteeing comparability to the text-coverage threshold. Such methods involve a computationally complex problem, for which we also propose a practical algorithm. In addition, we propose a neural-network based classifier from which we can obtain better uncertainty values. For evaluation, we created a crowdsourcing-based dataset in which a learner takes both vocabulary and readability tests. The best method under our framework outperformed conventional methods.
{"title":"Uncertainty-Aware Personalized Readability Assessments for Second Language Learners","authors":"Yo Ehara","doi":"10.1109/ICMLA.2019.00307","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00307","url":null,"abstract":"Assessing whether an ungraded second language learner can read a given text quickly is important for further instructing and supporting the learner, particularly when evaluating numerous ungraded learners from diverse backgrounds. Second language acquisition (SLA) studies have tackled such assessment tasks wherein only a single short vocabulary test result is available to assess a learner; such studies have shown that the text-coverage, i.e., the percentage of words the learner knows in the text, is the key assessment measure. Currently, count-based percentages are used, in which each word in the given text is classified as being known or unknown to the learner, and the words classified as known are then simply counted. When each word is classified, we can also obtain an uncertainty value as to how likely each word is known to the learner. Although such values can be informative for a readability assessment, how to leverage these values to guarantee their use as an assessment measure that is comparable to that of the previous values remains unclear. We propose a novel framework that allows assessment methods to be uncertainty-aware while guaranteeing comparability to the text-coverage threshold. Such methods involve a computationally complex problem, for which we also propose a practical algorithm. In addition, we propose a neural-network based classifier from which we can obtain better uncertainty values. For evaluation, we created a crowdsourcing-based dataset in which a learner takes both vocabulary and readability tests. The best method under our framework outperformed conventional methods.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114474099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00283
Ghada Zamzmi, L. Hsu, Wen Li, V. Sachdev, Sameer Kiran Antani
Doppler Echocardiography is critical for measuring abnormal cardiac function and diagnosing valvular stenosis and regurgitation. The current practice for assessing and interpreting Doppler echo images is time-consuming and depends highly on the experience of the operator. The limitations of this practice can be mitigated using fully automated intelligent systems. Essential first steps toward comprehensive computer-assisted Doppler echocardiographic interpretation include automatic classification into view/flow categories and goodness assessment of these flows. In this paper, we propose a deep learning-based method for Doppler flow classification and goodness assessment. The method has been trained on labeled images representing a wide range of real-world clinical variation. Our method, when evaluated on unseen data, achieved overall accuracies of 91.6% and 88.9% for flow classification and goodness assessment, respectively. While further research is needed, these results are encouraging and prove the feasibility of using fully automated intelligent systems for analyzing and interpreting Doppler echo images.
{"title":"Echo Doppler Flow Classification and Goodness Assessment with Convolutional Neural Networks","authors":"Ghada Zamzmi, L. Hsu, Wen Li, V. Sachdev, Sameer Kiran Antani","doi":"10.1109/ICMLA.2019.00283","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00283","url":null,"abstract":"Doppler Echocardiography is critical for measuring abnormal cardiac function and diagnosing valvular stenosis and regurgitation. The current practice for assessing and interpreting Doppler echo images is time-consuming and depends highly on the experience of the operator. The limitations of this practice can be mitigated using fully automated intelligent systems. Essential first steps toward comprehensive computer-assisted Doppler echocardiographic interpretation include automatic classification into view/flow categories and goodness assessment of these flows. In this paper, we propose a deep learning-based method for Doppler flow classification and goodness assessment. The method has been trained on labeled images representing a wide range of real-world clinical variation. Our method, when evaluated on unseen data, achieved overall accuracies of 91.6% and 88.9% for flow classification and goodness assessment, respectively. While further research is needed, these results are encouraging and prove the feasibility of using fully automated intelligent systems for analyzing and interpreting Doppler echo images.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123006048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICMLA.2019.00134
Justin M. Johnson, T. Khoshgoftaar
Class imbalance is a regularly occurring problem in machine learning that has been studied extensively over the last two decades. Various methods for addressing class imbalance have been introduced, including algorithm-level methods, datalevel methods, and hybrid methods. While these methods are well studied using traditional machine learning algorithms, there are relatively few studies that explore their application to deep neural networks. Thresholding, in particular, is rarely discussed in the deep learning with class imbalance literature. This paper addresses this gap by conducting a systematic study on the application of thresholding with deep neural networks using a Big Data Medicare fraud data set. We use random oversampling (ROS), random under-sampling (RUS), and a hybrid ROS-RUS to create 15 training distributions with varying levels of class imbalance. With the fraudulent class size ranging from 0.03%–60%, we identify optimal classification thresholds for each distribution on random validation sets and then score the thresholds on a 20% holdout test set. Through repetition and statistical analysis, confidence intervals show that the default threshold is never optimal when training data is imbalanced. Results also show that the optimal threshold outperforms the default threshold in nearly all cases, and linear models indicate a strong linear relationship between the minority class size and the optimal decision threshold. To the best of our knowledge, this is the first study to provide statistical results that describe optimal classification thresholds for deep neural networks over a range of class distributions.
{"title":"Deep Learning and Thresholding with Class-Imbalanced Big Data","authors":"Justin M. Johnson, T. Khoshgoftaar","doi":"10.1109/ICMLA.2019.00134","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00134","url":null,"abstract":"Class imbalance is a regularly occurring problem in machine learning that has been studied extensively over the last two decades. Various methods for addressing class imbalance have been introduced, including algorithm-level methods, datalevel methods, and hybrid methods. While these methods are well studied using traditional machine learning algorithms, there are relatively few studies that explore their application to deep neural networks. Thresholding, in particular, is rarely discussed in the deep learning with class imbalance literature. This paper addresses this gap by conducting a systematic study on the application of thresholding with deep neural networks using a Big Data Medicare fraud data set. We use random oversampling (ROS), random under-sampling (RUS), and a hybrid ROS-RUS to create 15 training distributions with varying levels of class imbalance. With the fraudulent class size ranging from 0.03%–60%, we identify optimal classification thresholds for each distribution on random validation sets and then score the thresholds on a 20% holdout test set. Through repetition and statistical analysis, confidence intervals show that the default threshold is never optimal when training data is imbalanced. Results also show that the optimal threshold outperforms the default threshold in nearly all cases, and linear models indicate a strong linear relationship between the minority class size and the optimal decision threshold. To the best of our knowledge, this is the first study to provide statistical results that describe optimal classification thresholds for deep neural networks over a range of class distributions.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114950196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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