Robust Ensemble Classifier Combination Based on Noise Removal with One-Class SVM

TL;DR

This paper proposes a noise removal method using One-Class SVM during data partitioning to improve ensemble classifier performance on large datasets.

Contribution

It introduces a noise filtering preprocessing step with One-Class SVM for data partitioning, enhancing ensemble classifier accuracy.

Findings

Noise filtering improves classifier performance.

Gini impurity guides optimal noise filter ratio.

Enhanced ensemble models outperform baseline methods.

Abstract

In machine learning area, as the number of labeled input samples becomes very large, it is very difficult to build a classification model because of input data set is not fit in a memory in training phase of the algorithm, therefore, it is necessary to utilize data partitioning to handle overall data set. Bagging and boosting based data partitioning methods have been broadly used in data mining and pattern recognition area. Both of these methods have shown a great possibility for improving classification model performance. This study is concerned with the analysis of data set partitioning with noise removal and its impact on the performance of multiple classifier models. In this study, we propose noise filtering preprocessing at each data set partition to increment classifier model performance. We applied Gini impurity approach to find the best split percentage of noise filter ratio. The filtered sub data set is then used to train individual ensemble models.