An adaptive learning paradigm: event detection through a novel dynamic arithmetic optimization-based ensemble SVM for data stream classification
Data stream mining is the process of generating continuous data stream records such as internet search, phone conversations, sensor data, etc. However it performs huge tasks such as frequency counting, clustering, analysis as well as classification. Mining information from data streams is often cons...
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| Vydáno v: | International journal of information technology (Singapore. Online) Ročník 16; číslo 5; s. 3049 - 3055 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Singapore
Springer Nature Singapore
01.06.2024
Springer Nature B.V |
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| ISSN: | 2511-2104, 2511-2112 |
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| Abstract | Data stream mining is the process of generating continuous data stream records such as internet search, phone conversations, sensor data, etc. However it performs huge tasks such as frequency counting, clustering, analysis as well as classification. Mining information from data streams is often considered as a complicated process due to the rapid change in the underlying concept which is often referred to as concept drift and the high speed of data arrival. Moreover the data stream classification process is not stationary where each transmission is evolved with time. In addition to this, it cannot able to handle imbalanced data and is not able to accommodate new classes. To overcome this problem, an Ensemble Learning model based Support Vector Machine (ESVM) is proposed to perform the data stream classification. To achieve higher diversity, each base SVM is trained with different feature subsets and updated during the presence of new data instances. However, the selection of optimal feature subsets from high dimensional data streams is complex due to the increase in size and computational cost. Hence Dynamic Accelerated Function (DAF) and Dynamic Candidate Solution (DCS) approaches are developed that diminish the classification error and improve the performance with the best fitness value. The performances of the proposed methods is validated based on accuracy, precision, F-score, kappa, and relative error. The experimental result demonstrates that the proposed model is efficient when evaluated in terms of classification accuracy, rapid training, processing time, kappa score and attained an accuracy of 91.45%. |
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| AbstractList | Data stream mining is the process of generating continuous data stream records such as internet search, phone conversations, sensor data, etc. However it performs huge tasks such as frequency counting, clustering, analysis as well as classification. Mining information from data streams is often considered as a complicated process due to the rapid change in the underlying concept which is often referred to as concept drift and the high speed of data arrival. Moreover the data stream classification process is not stationary where each transmission is evolved with time. In addition to this, it cannot able to handle imbalanced data and is not able to accommodate new classes. To overcome this problem, an Ensemble Learning model based Support Vector Machine (ESVM) is proposed to perform the data stream classification. To achieve higher diversity, each base SVM is trained with different feature subsets and updated during the presence of new data instances. However, the selection of optimal feature subsets from high dimensional data streams is complex due to the increase in size and computational cost. Hence Dynamic Accelerated Function (DAF) and Dynamic Candidate Solution (DCS) approaches are developed that diminish the classification error and improve the performance with the best fitness value. The performances of the proposed methods is validated based on accuracy, precision, F-score, kappa, and relative error. The experimental result demonstrates that the proposed model is efficient when evaluated in terms of classification accuracy, rapid training, processing time, kappa score and attained an accuracy of 91.45%. |
| Author | Vidya, R. Mary Ramakrishna, M. |
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| Cites_doi | 10.1007/s41870-023-01583-2 10.1016/j.inffus.2018.01.003 10.1007/s10489-018-1280-5 10.1007/s11390-020-9999-y 10.1007/s11704-010-0508-2 10.1007/s41870-023-01499-x 10.1016/j.knosys.2018.09.032 10.1007/s41870-017-0036-5 10.1109/ACCESS.2022.3146374 10.1016/j.asoc.2021.107378 10.1007/s41870-023-01416-2 10.1007/s41870-023-01506-1 |
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| Copyright | Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. |
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| DOI | 10.1007/s41870-024-01832-y |
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| SubjectTerms | Accuracy Algorithms Artificial Intelligence Classification Clustering Computer Imaging Computer Science Data transmission Ensemble learning Image Processing and Computer Vision Machine Learning Optimization Original Research Pattern Recognition and Graphics Software Engineering Support vector machines Vision |
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| Title | An adaptive learning paradigm: event detection through a novel dynamic arithmetic optimization-based ensemble SVM for data stream classification |
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