Novel Grouping Method-based support vector machine plus for structured data

In the traditional Support Vector Machine Plus (SVM+), the grouping method has great randomness and only takes into account part of the structural information of the dataset. In order to overcome these shortcomings, in this paper, we propose a novel framework termed as FCSVM+ to improve the performance of SVM+ by combining clustering technique and feature selection. The new framework strategy is expected to not only take fully into account the structural information of the training data, but also partition the training data into more meaningful groups. To prove the advantage of the framework, in particular, we adopt two simplest feature selection methods, i.e. F-score and Laplacian score methods, to select the features, then apply a recently proposed clustering technique to get a better partition of training data by the selected features, in which the number of clusters could be found automatically. Three major contributions of this paper can be concluded as: (1) improving the performance of the existing SVM+ classifier; (2) extracting the potential structural information of the training data by using more feature attributes instead of one; (3) replacing the truncation method in SVM+ with a clustering technique. The comprehensive experimental results on the UCI benchmark datasets illustrate the validity and advantage of our approach.