Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | null | Journal of Multiple-Valued Logic and Soft Computing |
Keywords | Field | DocType |
classifier integration approach,differentiated sampling rate algorithm,adaboost algorithm,ensemble-based learning algorithm,learning (artificial intelligence),pattern classification,classifier-selection strategy,svm-c5.0,differentiated sampling rate,imbalanced datasets,heterogeneous classifier,ensemble model of classifiers,classification in imbalanced datasets,data mining,scecm,classification,classification decision-making method,machine learning,ensemble classifier model,support vector machines,learning artificial intelligence,accuracy,classification algorithms | Data set,Margin (machine learning),Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Statistical classification,Margin classifier,Classifier (linguistics),Ensemble learning,Machine learning,Quadratic classifier | Journal |
Volume | Issue | ISSN |
18 | 5-6 | null |
ISBN | Citations | PageRank |
978-1-4244-6791-4 | 1 | 0.35 |
References | Authors | |
12 | 4 | |