A General Wrapper Approach to Selection of Class-Dependent Features

In this paper, we argue that for a C-class classification problem, C 2-class classifiers, each of which discriminating one class from the other classes and having a characteristic input feature subset, should in general outperform, or at least match the performance of, a C-class classifier with one single input feature subset. For each class, we select a desirable feature subset, which leads to the lowest classification error rate for this class using a classifier for a given feature subset search algorithm. To fairly compare all models, we propose a weight method for the class-dependent classifier, i.e., assigning a weight to each model's output before the comparison is carried out. The method's performance is evaluated on two artificial data sets and several real-world benchmark data sets, with the support vector machine (SVM) as the classifier, and with the RELIEF, class separability, and minimal-redundancy

• Type: Journal
• Year: 2008
• Where: TNN
• Authors: Lipo Wang, Nina Zhou, Feng Chu
• Keywords: C-class Classification Problem, Feature Subsets, Lowest Classification Error, TNN 2008