Pub Date : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00277
Mashael Al-Luhaybi, S. Swift, S. Counsell, A. Tucker
This paper addresses the problem that many machine learning classifiers make decisions based on data that are biased and can therefore result in prejudiced decisions. For example, in education (which this paper focuses on) a student may be rejected from a course based on historical decisions in the data that only exist due to historical biases in society or due to the skewed sampling of the data. Other approaches to dealing with bias in data include resampling methods (to counter imbalanced samples) and dimensionality reduction (to focus only on relevant features to the classification task). In this paper, we explore issues of modelling bias explicitly so that we can identify the types of bias and whether they are accounting for inflated predictive accuracies. In particular, we compare graphical model approaches to building classifiers, that are transparent in how they make decisions, with two forms of Deep Multi-label Convolutional Neural Networks to investigate if models can be built that maximise accuracy and minimise bias. We carry out this comparison on student entry and performance data from a higher educational institution.
{"title":"Exploring the Explicit Modelling of Bias in Machine Learning Classifiers: A Deep Multi-label ConvNet Approach *","authors":"Mashael Al-Luhaybi, S. Swift, S. Counsell, A. Tucker","doi":"10.1109/ICMLA55696.2022.00277","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00277","url":null,"abstract":"This paper addresses the problem that many machine learning classifiers make decisions based on data that are biased and can therefore result in prejudiced decisions. For example, in education (which this paper focuses on) a student may be rejected from a course based on historical decisions in the data that only exist due to historical biases in society or due to the skewed sampling of the data. Other approaches to dealing with bias in data include resampling methods (to counter imbalanced samples) and dimensionality reduction (to focus only on relevant features to the classification task). In this paper, we explore issues of modelling bias explicitly so that we can identify the types of bias and whether they are accounting for inflated predictive accuracies. In particular, we compare graphical model approaches to building classifiers, that are transparent in how they make decisions, with two forms of Deep Multi-label Convolutional Neural Networks to investigate if models can be built that maximise accuracy and minimise bias. We carry out this comparison on student entry and performance data from a higher educational institution.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129808166","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 : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00142
Francois Buet-Golfouse, Parth Pahwa
Multi-output supervised learning and multi-task learning are all instances of a broader learning paradigm where features, parameters and objectives are shared to a certain extent. Examples of such approaches include reusing features from pre-existing models in a new algorithm, performing multi-label regression or optimising for several tasks jointly. In this paper, we address this challenge by devising a generic framework based on generalised low-rank models ("GLRMs"), which include – broadly speaking– most techniques that can be expressed in terms of matrix factorisation. Importantly, while GLRMs first and foremost tackle unsupervised learning problems and supervised linear models. Here, we show that GLRMs can be extended by introducing multivariate functionals and structure regularisation terms to handle multivariate learning. This paper also proposes a coherent framework to design multi-learning strategies and covers existing algorithms. Finally, we prove the simplicity and effectiveness of our approach on empirical data.
{"title":"Multi-Learning Generalised Low-Rank Models","authors":"Francois Buet-Golfouse, Parth Pahwa","doi":"10.1109/ICMLA55696.2022.00142","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00142","url":null,"abstract":"Multi-output supervised learning and multi-task learning are all instances of a broader learning paradigm where features, parameters and objectives are shared to a certain extent. Examples of such approaches include reusing features from pre-existing models in a new algorithm, performing multi-label regression or optimising for several tasks jointly. In this paper, we address this challenge by devising a generic framework based on generalised low-rank models (\"GLRMs\"), which include – broadly speaking– most techniques that can be expressed in terms of matrix factorisation. Importantly, while GLRMs first and foremost tackle unsupervised learning problems and supervised linear models. Here, we show that GLRMs can be extended by introducing multivariate functionals and structure regularisation terms to handle multivariate learning. This paper also proposes a coherent framework to design multi-learning strategies and covers existing algorithms. Finally, we prove the simplicity and effectiveness of our approach on empirical data.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121186987","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 : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00203
Sang-Hun Sim, Tara Paranjpe, Nicole Roberts, Ming Zhao
Stress is a central factor in our daily lives, impacting performance, decisions, well-being, and our interactions with others. With the development of IoT technology, smart wearable devices can handle diverse operations, including networking and recording biometric signals. The enhanced data processing capability of wearables has also allowed for increased stress awareness among users. Edge computing on such devices enables real-time feedback which can provide an opportunity to prevent severe consequences that might result if stress is left unaddressed. Edge computing can also strengthen privacy by implementing stress prediction on local devices without transferring personal information to the public cloud.This paper presents a framework for real-time stress prediction, specifically for police training cadets, using wearable devices and machine learning with support from cloud computing. We developed an application for Fitbit and the user's accompanying smartphone to collect heart rate fluctuations and corresponding stress levels entered by users and a cloud backend for storing data and training models. Real-world data for this study was collected from police cadets during a police academy training program. Machine learning classifiers for stress prediction were built using this data through classic machine learning models and neural networks. To analyze efficiency across different environments, the models were optimized using model compression and other relevant techniques and tested on cloud and edge environments. Evaluation using real data and real devices showed that the highest accuracy came from XGBoost and Tensorflow neural network models, and on-edge stress prediction models produced lower latency results than in-cloud prediction.
{"title":"Exploring Edge Machine Learning-based Stress Prediction using Wearable Devices","authors":"Sang-Hun Sim, Tara Paranjpe, Nicole Roberts, Ming Zhao","doi":"10.1109/ICMLA55696.2022.00203","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00203","url":null,"abstract":"Stress is a central factor in our daily lives, impacting performance, decisions, well-being, and our interactions with others. With the development of IoT technology, smart wearable devices can handle diverse operations, including networking and recording biometric signals. The enhanced data processing capability of wearables has also allowed for increased stress awareness among users. Edge computing on such devices enables real-time feedback which can provide an opportunity to prevent severe consequences that might result if stress is left unaddressed. Edge computing can also strengthen privacy by implementing stress prediction on local devices without transferring personal information to the public cloud.This paper presents a framework for real-time stress prediction, specifically for police training cadets, using wearable devices and machine learning with support from cloud computing. We developed an application for Fitbit and the user's accompanying smartphone to collect heart rate fluctuations and corresponding stress levels entered by users and a cloud backend for storing data and training models. Real-world data for this study was collected from police cadets during a police academy training program. Machine learning classifiers for stress prediction were built using this data through classic machine learning models and neural networks. To analyze efficiency across different environments, the models were optimized using model compression and other relevant techniques and tested on cloud and edge environments. Evaluation using real data and real devices showed that the highest accuracy came from XGBoost and Tensorflow neural network models, and on-edge stress prediction models produced lower latency results than in-cloud prediction.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121257224","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 : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00017
Olaoluwa Adigun, B. Kosko
The new NoVa hidden neurons have outperformed ReLU hidden neurons in deep classifiers on some large image test sets. The NoVa or nonvanishing logistic neuron additively perturbs the sigmoidal activation function so that its derivative is not zero. This helps avoid or delay the problem of vanishing gradients. We here extend the NoVa to the generalized perturbed logistic neuron and compare it to ReLU and several other hidden neurons on large image test sets that include CIFAR-100 and Caltech-256. Generalized NoVa classifiers allow deeper networks with better classification on the large datasets. This deep benefit holds for ordinary unidirectional backpropagation. It also holds for the more efficient bidirectional backpropagation that trains in both the forward and backward directions.
{"title":"Deeper Bidirectional Neural Networks with Generalized Non-Vanishing Hidden Neurons","authors":"Olaoluwa Adigun, B. Kosko","doi":"10.1109/ICMLA55696.2022.00017","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00017","url":null,"abstract":"The new NoVa hidden neurons have outperformed ReLU hidden neurons in deep classifiers on some large image test sets. The NoVa or nonvanishing logistic neuron additively perturbs the sigmoidal activation function so that its derivative is not zero. This helps avoid or delay the problem of vanishing gradients. We here extend the NoVa to the generalized perturbed logistic neuron and compare it to ReLU and several other hidden neurons on large image test sets that include CIFAR-100 and Caltech-256. Generalized NoVa classifiers allow deeper networks with better classification on the large datasets. This deep benefit holds for ordinary unidirectional backpropagation. It also holds for the more efficient bidirectional backpropagation that trains in both the forward and backward directions.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116845885","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}
Anomaly Detection is a widely used technique in machine learning that identifies context-specific outliers. Most real-world anomaly detection applications are unsupervised, owing to the bottleneck of obtaining labeled data for a given context. In this paper, we solve two important problems pertaining to unsupervised anomaly detection. First, we identify only the most informative subsets of data points and obtain ground truths from the domain expert (oracle); second, we perform efficient model selection using a Bayesian Inference framework and recommend the top-k models to be fine-tuned prior to deployment. To this end, we exploit multiple existing and novel acquisition functions, and successfully demonstrate the effectiveness of the proposed framework using a weighted Ranking Score (η) to accurately rank the top-k models. Our empirical results show a significant reduction in data points acquired (with at least 60% reduction) while not compromising on the efficiency of the top-k models chosen, with both uniform and non-uniform priors over models.
{"title":"Data-Efficient Automatic Model Selection in Unsupervised Anomaly Detection","authors":"Gautham Krishna Gudur, Raaghul R, Adithya K, Shrihari Vasudevan","doi":"10.1109/ICMLA55696.2022.00227","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00227","url":null,"abstract":"Anomaly Detection is a widely used technique in machine learning that identifies context-specific outliers. Most real-world anomaly detection applications are unsupervised, owing to the bottleneck of obtaining labeled data for a given context. In this paper, we solve two important problems pertaining to unsupervised anomaly detection. First, we identify only the most informative subsets of data points and obtain ground truths from the domain expert (oracle); second, we perform efficient model selection using a Bayesian Inference framework and recommend the top-k models to be fine-tuned prior to deployment. To this end, we exploit multiple existing and novel acquisition functions, and successfully demonstrate the effectiveness of the proposed framework using a weighted Ranking Score (η) to accurately rank the top-k models. Our empirical results show a significant reduction in data points acquired (with at least 60% reduction) while not compromising on the efficiency of the top-k models chosen, with both uniform and non-uniform priors over models.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117251164","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 : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00026
Frederick Mubang, Lawrence O. Hall
The Volume Audience Match Simulator is an end-to-end approach for predicting user-to-user interactions on a given social media platform. It is comprised of 2 components: firstly, an XGBoost-driven volume prediction module that predicts the number of: (1) total activities, (2) active old users, and (3) newly active users over the span of 24 hours from the start time of prediction. Secondly, VAM contains a User-Assignment Module that takes as input the volume predictions and predicts the user-to-user interactions of the old and new users.In previous work, VAM has been used to predict Twitter discussions related to political crises. In this work, VAM was used to predict future activity on Twitter related to international economic affairs. We include more experiments and analyses than previous work performed with VAM. In this work, VAM is used to predict all types of retweets, including quotes and replies, unlike previous work, which only focused on regular retweets. Furthermore, we show that YouTube features, in addition to Reddit features can improve prediction performance. We examine the importance of the time series features used in VAM’s Volume Prediction module. Lastly, we show that VAM’s performance is significantly more accurate than other approaches when predicting highly-skewed, lowly-skewed, highly-sparse, and lowly-sparse time series.
{"title":"Simulating New and Old Twitter User Activity with XGBoost and Probabilistic Hybrid Models","authors":"Frederick Mubang, Lawrence O. Hall","doi":"10.1109/ICMLA55696.2022.00026","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00026","url":null,"abstract":"The Volume Audience Match Simulator is an end-to-end approach for predicting user-to-user interactions on a given social media platform. It is comprised of 2 components: firstly, an XGBoost-driven volume prediction module that predicts the number of: (1) total activities, (2) active old users, and (3) newly active users over the span of 24 hours from the start time of prediction. Secondly, VAM contains a User-Assignment Module that takes as input the volume predictions and predicts the user-to-user interactions of the old and new users.In previous work, VAM has been used to predict Twitter discussions related to political crises. In this work, VAM was used to predict future activity on Twitter related to international economic affairs. We include more experiments and analyses than previous work performed with VAM. In this work, VAM is used to predict all types of retweets, including quotes and replies, unlike previous work, which only focused on regular retweets. Furthermore, we show that YouTube features, in addition to Reddit features can improve prediction performance. We examine the importance of the time series features used in VAM’s Volume Prediction module. Lastly, we show that VAM’s performance is significantly more accurate than other approaches when predicting highly-skewed, lowly-skewed, highly-sparse, and lowly-sparse time series.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116804837","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 : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00029
Ana Carolina Rodrigues, R. Marcacini
Distinct pre-trained embedding models perform differently in sentence similarity recognition tasks. The current assumption is that they encode different features due to differences in algorithm design and characteristics of the datasets employed in the pre-trained process. The perspective of benefiting from different encoded features to generate more suitable representations motivated the assembly of multiple embedding models, so-called meta-embedding. Meta-embedding methods combine different pre-trained embedding models to perform a task. Recently, multiple pre-trained language representations derived from Transformers architecture-based systems have been shown to be effective in many downstream tasks. This paper introduces a supervised meta-embedding neural network to combine contextualized pre-trained models for sentence similarity recognition in Portuguese. Our results show that combining multiple sentence pre-trained embedding models outperforms single models and can be a promising alternative to improve performance sentence similarity. Moreover, we also discuss the results considering our simple extension of a model explainability method to the meta-embedding context, allowing the visual identification of the impact of each token on the sentence similarity score.
{"title":"Sentence Similarity Recognition in Portuguese from Multiple Embedding Models","authors":"Ana Carolina Rodrigues, R. Marcacini","doi":"10.1109/ICMLA55696.2022.00029","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00029","url":null,"abstract":"Distinct pre-trained embedding models perform differently in sentence similarity recognition tasks. The current assumption is that they encode different features due to differences in algorithm design and characteristics of the datasets employed in the pre-trained process. The perspective of benefiting from different encoded features to generate more suitable representations motivated the assembly of multiple embedding models, so-called meta-embedding. Meta-embedding methods combine different pre-trained embedding models to perform a task. Recently, multiple pre-trained language representations derived from Transformers architecture-based systems have been shown to be effective in many downstream tasks. This paper introduces a supervised meta-embedding neural network to combine contextualized pre-trained models for sentence similarity recognition in Portuguese. Our results show that combining multiple sentence pre-trained embedding models outperforms single models and can be a promising alternative to improve performance sentence similarity. Moreover, we also discuss the results considering our simple extension of a model explainability method to the meta-embedding context, allowing the visual identification of the impact of each token on the sentence similarity score.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115512781","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 : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00201
Lars Væhrens, D. D. Álvarez, U. Berger, Simon Boegh
We present a deep reinforcement learning-based approach to control robotic manipulators and construct task-independent trajectories for point-to-point motions. The research objective in this work is to learn control in the joint action space, which can be generalized to various industrial manipulators. The approach necessitates that the neural network learns a mapping from joint movements to the reward landscape determined by the distance to the goal and nearby obstacles. In addition, curriculum learning is embedded in this approach to facilitate learning by reducing the complexity of the environment. Conducted experiments demonstrate how the reinforcement learning-based approach can be applied to three different industrial manipulators in simulation with minimal configuration changes. The results of our contribution demonstrate that a model can be trained in a simulation environment, transferred to the real world, and used in complex environments. Furthermore, the Sim2Real transfer, augmented by curriculum learning, highlights that the robots behave in the same way in the real world as in the simulation and that the operations in the real world are safe from a control and trajectory point-of-view.
{"title":"Learning Task-independent Joint Control for Robotic Manipulators with Reinforcement Learning and Curriculum Learning","authors":"Lars Væhrens, D. D. Álvarez, U. Berger, Simon Boegh","doi":"10.1109/ICMLA55696.2022.00201","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00201","url":null,"abstract":"We present a deep reinforcement learning-based approach to control robotic manipulators and construct task-independent trajectories for point-to-point motions. The research objective in this work is to learn control in the joint action space, which can be generalized to various industrial manipulators. The approach necessitates that the neural network learns a mapping from joint movements to the reward landscape determined by the distance to the goal and nearby obstacles. In addition, curriculum learning is embedded in this approach to facilitate learning by reducing the complexity of the environment. Conducted experiments demonstrate how the reinforcement learning-based approach can be applied to three different industrial manipulators in simulation with minimal configuration changes. The results of our contribution demonstrate that a model can be trained in a simulation environment, transferred to the real world, and used in complex environments. Furthermore, the Sim2Real transfer, augmented by curriculum learning, highlights that the robots behave in the same way in the real world as in the simulation and that the operations in the real world are safe from a control and trajectory point-of-view.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116249232","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 : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00192
Boaz Ilan, A. Ranganath, Jacqueline Alvarez, Shilpa Khatri, Roummel F. Marcia
Interpretability continues to be a focus of much research in deep neural network. In this work, we focus on the mathematical interpretability of fully-connected neural networks, especially those that use a rectified linear unit (ReLU) activation function. Our analysis elucidates the difficulty of approximating the reciprocal function. Notwithstanding, using the ReLU activation function halves the error compared with a linear model. In addition, one might have expected the errors to increase only towards the singular point x = 0, but both the linear and ReLU errors are fairly oscillatory and increase near both edge points.
{"title":"Interpretability of ReLU for Inversion","authors":"Boaz Ilan, A. Ranganath, Jacqueline Alvarez, Shilpa Khatri, Roummel F. Marcia","doi":"10.1109/ICMLA55696.2022.00192","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00192","url":null,"abstract":"Interpretability continues to be a focus of much research in deep neural network. In this work, we focus on the mathematical interpretability of fully-connected neural networks, especially those that use a rectified linear unit (ReLU) activation function. Our analysis elucidates the difficulty of approximating the reciprocal function. Notwithstanding, using the ReLU activation function halves the error compared with a linear model. In addition, one might have expected the errors to increase only towards the singular point x = 0, but both the linear and ReLU errors are fairly oscillatory and increase near both edge points.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128063550","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 : 2022-12-01DOI: 10.1109/ICMLA55696.2022.00191
Mehmet Akif Gulum, Christopher M. Trombley, M. Ozen, M. Kantardzic
Deep learning has demonstrated impressive performance for medical tasks such as cancer classification and lesion detection. While it has achieved impressive performance, it is a black-box algorithm and therefore is difficult to interpret. Interpretation is especially important in fields that are high-risk in nature such as the medical field. There recently has been various methods proposed to interpret deep learning algorithms. However, there are limited studies evaluating these explanation methods in clinical settings such as radiology. To that end, we conduct a pilot study that evaluates the effectiveness of explanation methods for radiology end use. We evaluate if explanation methods improve diagnosis performance and what method is preferred by radiologists. We also glean insight into what characteristics radiologists deem explainable. We found that explanation methods increase diagnosis performance however it is dependent on the individual method. We also find that the radiology cohort deem the themes insight, visualization, and accuracy to be the most sought after explainable characteristics. The insights garnered in this study have the potential to guide future developments and studies of explanation methods for clinical use.
{"title":"Are Post-Hoc Explanation Methods for Prostate Lesion Detection Effective for Radiology End Use?","authors":"Mehmet Akif Gulum, Christopher M. Trombley, M. Ozen, M. Kantardzic","doi":"10.1109/ICMLA55696.2022.00191","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00191","url":null,"abstract":"Deep learning has demonstrated impressive performance for medical tasks such as cancer classification and lesion detection. While it has achieved impressive performance, it is a black-box algorithm and therefore is difficult to interpret. Interpretation is especially important in fields that are high-risk in nature such as the medical field. There recently has been various methods proposed to interpret deep learning algorithms. However, there are limited studies evaluating these explanation methods in clinical settings such as radiology. To that end, we conduct a pilot study that evaluates the effectiveness of explanation methods for radiology end use. We evaluate if explanation methods improve diagnosis performance and what method is preferred by radiologists. We also glean insight into what characteristics radiologists deem explainable. We found that explanation methods increase diagnosis performance however it is dependent on the individual method. We also find that the radiology cohort deem the themes insight, visualization, and accuracy to be the most sought after explainable characteristics. The insights garnered in this study have the potential to guide future developments and studies of explanation methods for clinical use.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126718294","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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