Pub Date : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00070
L. Dheekollu, H. Wadhwa, Siddharth Vimal, Anubhav Gupta, Siddhartha Asthana, Ankur Arora, Smriti Gupta
Predicting customer attrition (churn) is a well known problem in industries that provide services, like financial institutions, telecommunications, e-commerce, and retail. There are two kinds of attrition - active and passive (silent). Active attrition is usually associated with subscription-based business models, commonly seen in telecommunications and internet industries like Netflix. In industries like finance, retail, and ecommerce, we see the other kind of attrition - silent attrition where customers stop doing business without formal notice. This makes the silent attrition prediction problem even more challenging because it is difficult to differentiate between attrited and inactive customers. We focus our work on predicting silent attrition which is still under-explored in the payment card industry (i.e. Mastercard, Visa). The contribution of our work is threefold. First, we present a data-driven approach to define silent attrition as customer inactivity. Second, we discussed multiple procedures to generate synthetic data thereby preserving customers’ privacy. At last, we presented a comprehensive view of various machine learning (ML) pathways in which this churn prediction problem can be framed and solved; each requiring a specific feature engineering. We presented experimental results corresponding to each pathway to comparative analysis. We believe that this work to be beneficial to the researchers and ML practitioners who often have to deal with sensitive financial data but have limited permission to use it. In this direction, we demonstrated the use of synthetic data generation to reduce the risk of data leakage and other privacy concerns relating to ML models development.
{"title":"Modeling approaches for Silent Attrition prediction in Payment networks","authors":"L. Dheekollu, H. Wadhwa, Siddharth Vimal, Anubhav Gupta, Siddhartha Asthana, Ankur Arora, Smriti Gupta","doi":"10.1109/ICMLA52953.2021.00070","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00070","url":null,"abstract":"Predicting customer attrition (churn) is a well known problem in industries that provide services, like financial institutions, telecommunications, e-commerce, and retail. There are two kinds of attrition - active and passive (silent). Active attrition is usually associated with subscription-based business models, commonly seen in telecommunications and internet industries like Netflix. In industries like finance, retail, and ecommerce, we see the other kind of attrition - silent attrition where customers stop doing business without formal notice. This makes the silent attrition prediction problem even more challenging because it is difficult to differentiate between attrited and inactive customers. We focus our work on predicting silent attrition which is still under-explored in the payment card industry (i.e. Mastercard, Visa). The contribution of our work is threefold. First, we present a data-driven approach to define silent attrition as customer inactivity. Second, we discussed multiple procedures to generate synthetic data thereby preserving customers’ privacy. At last, we presented a comprehensive view of various machine learning (ML) pathways in which this churn prediction problem can be framed and solved; each requiring a specific feature engineering. We presented experimental results corresponding to each pathway to comparative analysis. We believe that this work to be beneficial to the researchers and ML practitioners who often have to deal with sensitive financial data but have limited permission to use it. In this direction, we demonstrated the use of synthetic data generation to reduce the risk of data leakage and other privacy concerns relating to ML models development.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"64 1","pages":"409-414"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91086362","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 : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00180
Tyler Cody, Stephen C. Adams, P. Beling
Metric learning is a well-developed field in machine learning and has seen recent application in the area of prognostics and health management (PHM). Metric learning allows for fault diagnosis or condition monitoring models to be developed with the assumption that a machine- or load-specific similarity metric can be learned after model deployment. Existing literature has used metric learning to fine-tune deep learning models to address machine-to-machine differences and differences in working conditions. Here, we study metric learning in isolation, not as an intermediate step in deep learning, by conducting a comparative study of Principal Component Analysis (PCA), Neighborhood Component Analysis (NCA), Local Fisher Discriminant Analysis (LFDA), and Large Margin Nearest Neighbor (LMNN). We consider performance metrics for prediction performance, cluster performance, feature sensitivity, sample efficiency, and latent space efficiency. We find that linear partitions on the latent spaces learned via metric learning are able to achieve accuracies greater than 90% on Case Western Reserve University’s bearing fault data set using only the drive-end vibration signal. We find PCA to be dominated by metric learning algorithms for all working loads considered. And, in sum, we demonstrate classical metric learning algorithms to be a promising approach for learning machine-and load-specific similarity metrics for PHM with minor data processing and small samples.
{"title":"Trade-offs in Metric Learning for Bearing Fault Diagnosis","authors":"Tyler Cody, Stephen C. Adams, P. Beling","doi":"10.1109/ICMLA52953.2021.00180","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00180","url":null,"abstract":"Metric learning is a well-developed field in machine learning and has seen recent application in the area of prognostics and health management (PHM). Metric learning allows for fault diagnosis or condition monitoring models to be developed with the assumption that a machine- or load-specific similarity metric can be learned after model deployment. Existing literature has used metric learning to fine-tune deep learning models to address machine-to-machine differences and differences in working conditions. Here, we study metric learning in isolation, not as an intermediate step in deep learning, by conducting a comparative study of Principal Component Analysis (PCA), Neighborhood Component Analysis (NCA), Local Fisher Discriminant Analysis (LFDA), and Large Margin Nearest Neighbor (LMNN). We consider performance metrics for prediction performance, cluster performance, feature sensitivity, sample efficiency, and latent space efficiency. We find that linear partitions on the latent spaces learned via metric learning are able to achieve accuracies greater than 90% on Case Western Reserve University’s bearing fault data set using only the drive-end vibration signal. We find PCA to be dominated by metric learning algorithms for all working loads considered. And, in sum, we demonstrate classical metric learning algorithms to be a promising approach for learning machine-and load-specific similarity metrics for PHM with minor data processing and small samples.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"23 1","pages":"1100-1105"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82140624","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 : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00136
Aninditha Ramesh, Anusha S. Rao, Sanjana Moudgalya, K. S. Srinivas
Deep Learning models have been a tremendous breakthrough in the field of Drug discovery, greatly simplifying the pre-clinical phase of this intricate task. With an intention to ease this further, we introduce a novel method to generate target-specific molecules using a Generative Adversarial Network (GAN). The dataset consists of drugs whose target proteins belong to the class of Tyrosine kinase and are specifically active against some of the growth factor receptors present in the human body. An Autoencoder network is used to learn the embeddings of the drug which is represented in the SMILES format and the deep neural network GAN is used to generate structurally valid molecules using drug-target interaction as the validating criteria. The model has successfully produced 39 novel structures and 15 of them show satisfactory binding with at least one of the target receptors.
{"title":"GAN Based Approach for Drug Design","authors":"Aninditha Ramesh, Anusha S. Rao, Sanjana Moudgalya, K. S. Srinivas","doi":"10.1109/ICMLA52953.2021.00136","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00136","url":null,"abstract":"Deep Learning models have been a tremendous breakthrough in the field of Drug discovery, greatly simplifying the pre-clinical phase of this intricate task. With an intention to ease this further, we introduce a novel method to generate target-specific molecules using a Generative Adversarial Network (GAN). The dataset consists of drugs whose target proteins belong to the class of Tyrosine kinase and are specifically active against some of the growth factor receptors present in the human body. An Autoencoder network is used to learn the embeddings of the drug which is represented in the SMILES format and the deep neural network GAN is used to generate structurally valid molecules using drug-target interaction as the validating criteria. The model has successfully produced 39 novel structures and 15 of them show satisfactory binding with at least one of the target receptors.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"10 1","pages":"825-828"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81938131","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 : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00036
Yasutaka Furusho, Shuhei Nitta, Y. Sakata
Autoencoders (AEs) have been widely used for anomaly detection because models trained to reconstruct a normal data are expected to have a higher reconstruction error for anomalous data than that for normal data, and the higher error is adopted as a criterion for identifying anomalies. However, the high capacity of AEs is sometimes able to reconstruct anomalous data even when trained only on normal data, which leads to overlooked anomalies. To remedy this problem, we propose a kernel ridge reconstruction (KRR) approach for general, high-performance, and low computational anomaly detection. KRR replaces the non-linear decoder network of the AE with a linear regressor, which uses the weighted sum of training normal data for reconstruction, and thus prevents the reconstruction of anomalous data. We also reveal the desired property of the encoder for KRR to achieve high anomaly detection performance and propose an effective training algorithm to realize such property by instance discrimination and feature decorrelation. In addition, KRR reduces the computational cost because it replaces the non-linear decoder network with a linear regressor. Our experiments on MNIST, CIFAR10, and KDDCup99 datasets prove its applicability, high performance, and low computational cost. In particular, KRR achieved an area under the curve (AUC) of 0.670 with 12 millions multiply-accumulate operations (MACs) on the CIFAR10 dataset, outperforming a recent reconstruction-based anomaly detection method (MemAE) with a 1.1-fold higher AUC and 0.291 as many MACs.
{"title":"Kernel ridge reconstruction for anomaly detection: general and low computational reconstruction","authors":"Yasutaka Furusho, Shuhei Nitta, Y. Sakata","doi":"10.1109/ICMLA52953.2021.00036","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00036","url":null,"abstract":"Autoencoders (AEs) have been widely used for anomaly detection because models trained to reconstruct a normal data are expected to have a higher reconstruction error for anomalous data than that for normal data, and the higher error is adopted as a criterion for identifying anomalies. However, the high capacity of AEs is sometimes able to reconstruct anomalous data even when trained only on normal data, which leads to overlooked anomalies. To remedy this problem, we propose a kernel ridge reconstruction (KRR) approach for general, high-performance, and low computational anomaly detection. KRR replaces the non-linear decoder network of the AE with a linear regressor, which uses the weighted sum of training normal data for reconstruction, and thus prevents the reconstruction of anomalous data. We also reveal the desired property of the encoder for KRR to achieve high anomaly detection performance and propose an effective training algorithm to realize such property by instance discrimination and feature decorrelation. In addition, KRR reduces the computational cost because it replaces the non-linear decoder network with a linear regressor. Our experiments on MNIST, CIFAR10, and KDDCup99 datasets prove its applicability, high performance, and low computational cost. In particular, KRR achieved an area under the curve (AUC) of 0.670 with 12 millions multiply-accumulate operations (MACs) on the CIFAR10 dataset, outperforming a recent reconstruction-based anomaly detection method (MemAE) with a 1.1-fold higher AUC and 0.291 as many MACs.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"73 1","pages":"185-190"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76213444","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 : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00107
B. Paudel, Danushka Senarathna, Haibo Wang, S. Tragoudas, Yao Hu, Shengbing Jiang
Despite the immense performance improvement of deep learning-based object detection, the state-of-the-art object detection systems are still prone to misdetections. This work presents a method to predict such misdetections at run-time by using a small network, referred to as ConsensusNet, to learn the correlation patterns or consensus of neighboring detections before non-maximum suppression (NMS). Based on such correlations, ConsensusNet predicts if there are misdetection failures. The proposed method is experimentally evaluated considering single person class from COCO dataset and using YOLOv3 as the object detection system. It shows the proposed method can achieve accuracy of 84.6% and the performance measured in other metrics are also promising. To the best of our knowledge, ConsensusNet is the first network reported for predicting misdetections in object detection.
{"title":"Predicting YOLO Misdetection by Learning Grid Cell Consensus","authors":"B. Paudel, Danushka Senarathna, Haibo Wang, S. Tragoudas, Yao Hu, Shengbing Jiang","doi":"10.1109/ICMLA52953.2021.00107","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00107","url":null,"abstract":"Despite the immense performance improvement of deep learning-based object detection, the state-of-the-art object detection systems are still prone to misdetections. This work presents a method to predict such misdetections at run-time by using a small network, referred to as ConsensusNet, to learn the correlation patterns or consensus of neighboring detections before non-maximum suppression (NMS). Based on such correlations, ConsensusNet predicts if there are misdetection failures. The proposed method is experimentally evaluated considering single person class from COCO dataset and using YOLOv3 as the object detection system. It shows the proposed method can achieve accuracy of 84.6% and the performance measured in other metrics are also promising. To the best of our knowledge, ConsensusNet is the first network reported for predicting misdetections in object detection.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"134 1","pages":"643-648"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77894493","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 : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00280
Pouya Shiri, A. Baniasadi
Capsule Network (CapsNet) classifier has several advantages over CNNs, including better detection of images containing overlapping categories and higher accuracy on transformed images. Despite the advantages, CapsNet is slow due to its different structure. In addition, CapsNet is resource-hungry, includes many parameters and lags in accuracy compared to CNNs. In this work, we propose LE-CapsNet as a light, enhanced and more accurate variant of CapsNet. Using 3.8M weights, LECapsNet obtains 76.73% accuracy on the CIFAR-10 dataset while performing inference 4x faster than CapsNet. In addition, our proposed network is more robust at detecting images with affine transformations compared to CapsNet. We achieve 94.37% accuracy on the AffNIST dataset (compared to CapsNet’s 90.52%).
{"title":"LE-CapsNet: A Light and Enhanced Capsule Network","authors":"Pouya Shiri, A. Baniasadi","doi":"10.1109/ICMLA52953.2021.00280","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00280","url":null,"abstract":"Capsule Network (CapsNet) classifier has several advantages over CNNs, including better detection of images containing overlapping categories and higher accuracy on transformed images. Despite the advantages, CapsNet is slow due to its different structure. In addition, CapsNet is resource-hungry, includes many parameters and lags in accuracy compared to CNNs. In this work, we propose LE-CapsNet as a light, enhanced and more accurate variant of CapsNet. Using 3.8M weights, LECapsNet obtains 76.73% accuracy on the CIFAR-10 dataset while performing inference 4x faster than CapsNet. In addition, our proposed network is more robust at detecting images with affine transformations compared to CapsNet. We achieve 94.37% accuracy on the AffNIST dataset (compared to CapsNet’s 90.52%).","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"2 1","pages":"1767-1772"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73354851","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 : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00167
A. Hafeez, Eduardo Alonso, Aram Ter-Sarkisov
Predictive maintenance, which has traditionally used anomaly detection methods on sensory data, is now being replaced by event-based techniques. These methods utilise events with multiple temporal (and often non-numeric) features, produced by diagnostic modules. This raises the need of learning numerical event representations to predict the next fault event in industrial machines, specially vehicles, that use Diagnostic Trouble Codes (DTCs). We propose a predictive maintenance approach, named Sequential Multivariate Fault Prediction (SMFP), for predicting the next multivariate DTC fault in an event sequence, using Long Short-Term Memory Networks (LSTMs) and jointly learned event embeddings. By performing an in-depth comparison of different architectural choices and contextual preprocessing techniques, we provide an initial baseline for SMFP that achieves top-3 accuracy of 63% on predicting multivariate fault with 3 collective output layers, using vehicle maintenance data as a case study.
{"title":"Towards Sequential Multivariate Fault Prediction for Vehicular Predictive Maintenance","authors":"A. Hafeez, Eduardo Alonso, Aram Ter-Sarkisov","doi":"10.1109/ICMLA52953.2021.00167","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00167","url":null,"abstract":"Predictive maintenance, which has traditionally used anomaly detection methods on sensory data, is now being replaced by event-based techniques. These methods utilise events with multiple temporal (and often non-numeric) features, produced by diagnostic modules. This raises the need of learning numerical event representations to predict the next fault event in industrial machines, specially vehicles, that use Diagnostic Trouble Codes (DTCs). We propose a predictive maintenance approach, named Sequential Multivariate Fault Prediction (SMFP), for predicting the next multivariate DTC fault in an event sequence, using Long Short-Term Memory Networks (LSTMs) and jointly learned event embeddings. By performing an in-depth comparison of different architectural choices and contextual preprocessing techniques, we provide an initial baseline for SMFP that achieves top-3 accuracy of 63% on predicting multivariate fault with 3 collective output layers, using vehicle maintenance data as a case study.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"309 1","pages":"1016-1021"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75682546","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 : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00253
M. Fahim, S. Gokhale
Hateful and offensive speech on online social media platforms has seen a rise in the recent years. Often used to convey humor through sarcasm or to emphasize a point, offensive speech may also be employed to insult, deride and mock alternate points of view. In turbulent and chaotic circumstances, insults and mockery can lead to violence and unrest, and hence, such speech must be identified and tagged to limit its damage. This paper presents an application of machine learning to detect hateful and offensive content from Twitter feeds shared after the protests by Proud Boys, an extremist, ideological and violent hate group. A comprehensive coding guide, consolidating definitions of what constitutes offensive content based on the potential to trigger and incite people is developed and used to label the tweets. Linguistic, auxiliary and social features extracted from these labeled tweets were used to train machine learning classifiers, which detect offensive content with an accuracy of about 92%. An analysis of the importance scores reveals that offensiveness is pre-dominantly a function of words and their combinations, rather than meta features such as punctuations and quotes. This observation can form the foundation of pre-trained classifiers that can be deployed to automatically detect offensive speech in new and unforeseen circumstances.
{"title":"Detecting Offensive Content on Twitter During Proud Boys Riots","authors":"M. Fahim, S. Gokhale","doi":"10.1109/ICMLA52953.2021.00253","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00253","url":null,"abstract":"Hateful and offensive speech on online social media platforms has seen a rise in the recent years. Often used to convey humor through sarcasm or to emphasize a point, offensive speech may also be employed to insult, deride and mock alternate points of view. In turbulent and chaotic circumstances, insults and mockery can lead to violence and unrest, and hence, such speech must be identified and tagged to limit its damage. This paper presents an application of machine learning to detect hateful and offensive content from Twitter feeds shared after the protests by Proud Boys, an extremist, ideological and violent hate group. A comprehensive coding guide, consolidating definitions of what constitutes offensive content based on the potential to trigger and incite people is developed and used to label the tweets. Linguistic, auxiliary and social features extracted from these labeled tweets were used to train machine learning classifiers, which detect offensive content with an accuracy of about 92%. An analysis of the importance scores reveals that offensiveness is pre-dominantly a function of words and their combinations, rather than meta features such as punctuations and quotes. This observation can form the foundation of pre-trained classifiers that can be deployed to automatically detect offensive speech in new and unforeseen circumstances.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"44 3","pages":"1582-1587"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72631341","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 : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00100
Yuehan Yin, Yahya Alqahtani, Jinjuan Feng, J. Chakraborty, M. P. McGuire
Eye tracking data can help design effective user interfaces by showing how users visually process information. In this study, three neural network models were developed and employed to classify three types of information display methods by using eye gaze data that was collected in visual information processing behavior studies. Eye gaze data was first converted into a sequence and was fed into neural networks to predict the information display type. The results of the study show a comparison between three methods for the creation of eye tracking sequences and how they perform using three neural network models including CNN-LSTM, CNN-GRU, and 3D CNN. The results were positive with all models having an accuracy of higher than 88 percent.
{"title":"Deep Learning Methods for the Prediction of Information Display Type Using Eye Tracking Sequences","authors":"Yuehan Yin, Yahya Alqahtani, Jinjuan Feng, J. Chakraborty, M. P. McGuire","doi":"10.1109/ICMLA52953.2021.00100","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00100","url":null,"abstract":"Eye tracking data can help design effective user interfaces by showing how users visually process information. In this study, three neural network models were developed and employed to classify three types of information display methods by using eye gaze data that was collected in visual information processing behavior studies. Eye gaze data was first converted into a sequence and was fed into neural networks to predict the information display type. The results of the study show a comparison between three methods for the creation of eye tracking sequences and how they perform using three neural network models including CNN-LSTM, CNN-GRU, and 3D CNN. The results were positive with all models having an accuracy of higher than 88 percent.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"82 1","pages":"601-605"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72707683","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 : 2021-12-01DOI: 10.1109/ICMLA52953.2021.00088
Cagri Ozdemir, R. Hoover, Kyle A. Caudle
The tensor singular value decomposition (t-SVD) based on an algebra of circulants is an effective multilinear sub- space learning technique for dimensionality reduction and data classification. Unfortunately, the computational cost associated with computing the t-SVD can become prohibitively expensive, particularly when dealing with very large data sets. In this paper, we present a computationally efficient approach for estimating the t-SVD by capitalizing on the correlations of the data in the temporal dimension. The approach proceeds by extending our prior work on fast eigenspace decompositions by transforming the tensor data from the spatial domain to the spectral domain in order to obtain reduced order harmonic tensor. The t-SVD can then be applied in the transform domain thereby significantly reducing the computational burden. Experimental results which are presented on the extended Yale-B, COIL-100, and MNIST data sets show the proposed method provides considerable computational savings with the approximated subspaces that are nearly the same as the true subspaces as computed via the t-SVD.
{"title":"Fast Tensor Singular Value Decomposition Using the Low-Resolution Features of Tensors","authors":"Cagri Ozdemir, R. Hoover, Kyle A. Caudle","doi":"10.1109/ICMLA52953.2021.00088","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00088","url":null,"abstract":"The tensor singular value decomposition (t-SVD) based on an algebra of circulants is an effective multilinear sub- space learning technique for dimensionality reduction and data classification. Unfortunately, the computational cost associated with computing the t-SVD can become prohibitively expensive, particularly when dealing with very large data sets. In this paper, we present a computationally efficient approach for estimating the t-SVD by capitalizing on the correlations of the data in the temporal dimension. The approach proceeds by extending our prior work on fast eigenspace decompositions by transforming the tensor data from the spatial domain to the spectral domain in order to obtain reduced order harmonic tensor. The t-SVD can then be applied in the transform domain thereby significantly reducing the computational burden. Experimental results which are presented on the extended Yale-B, COIL-100, and MNIST data sets show the proposed method provides considerable computational savings with the approximated subspaces that are nearly the same as the true subspaces as computed via the t-SVD.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"64 1","pages":"527-533"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81380157","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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