A Novel Classifier - Weighted Features Cost-Sensitive SVM

Learning effective and efficient classifiers for imbalanced data is one of ten challenge problems in data mining research. Studying classifiers for imbalanced data is a popular area in machine learning and data mining, which also has great significance in many areas, such as cancer diagnose, credit card fraud detection and intrusion detection. The study for imbalanced data classification can be divided into three major parts: resampling data, change internal algorithm and cost-sensitive learning. Weighted feature methods can also enhance the accuracy of classification. In this paper, we intend to use a novel method combining weighted feature and cost-sensitive learning to deal with imbalanced data. We add weights to features, which causes that the position of each instance in space changes. Our goal is to increase the separation between classes by enlarging the space around the separating boundary surface through weighted features. Since the margin is enlarged, the chance that the instances in minority class are classified into majority class by mistake will be lower. Weighted Features cost-sensitive SVM (WF-CSSVM) performs well in both accuracy and cost. UCI datasets are utilized in experiment part and most of them can be classified perfectly. Accuracy, G-mean and ROC are employed as evaluation metrics.