Abstract
Learning in the presence of data imbalances presents a great challenge to machine learning. Imbalanced data sets represent a significant problem because the corresponding classifier has a tendency to ignore samples which have smaller
representation in the training sets. In this paper, we propose an ensemble-based learning algorithm as a new ensemble classifier model called as SVM-C5.0 Ensemble Classifier Model, SCECM. SCECM adopts a differentiated sampling rate algorithm (DSRA) based on an improved Adaboost algorithm and further employs
unique classifier-selection strategy, novel classifier integration approach and original classification decision-making method. Comparative experimental results show that the proposed approach improves performance for the minority class while preserving the ability to recognize examples from the majority classes.
Details
Original language | English |
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Title of host publication | Proceedings of 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering |
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Place of Publication | Beijing, China |
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Pages | 228-233 |
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Publication status | Published - Nov 2010 |
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Event | 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering - ISKE2010, Hangzhou, China Duration: 15 Nov 2010 → 16 Nov 2010 |
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Conference | 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering |
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Country | China |
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City | Hangzhou |
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Period | 2010-11-15 → 2010-11-16 |
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