A Novel Feature Selection Approach Using Classification Complexity for SVM of Stock Market Trend Prediction

As a preprocessing method of data mining with SVM, feature selection can eliminate irrelevant or redundant attributes and increase the density of samples in feature spaces that can improve classification performance. In the field of financial time series pattern recognition, the study of feature selection has been receiving increasing attention. Different from other studies, this work use two new coefficients: neighborhood dependence (ND) and neighborhood decision error (NDEM) to measure the classification complexity as a method of feature selection. In the real example, we use ASH, ASNN, ND and NDEM to measure the classification complexity of technical indicators data set of Shanghai stock exchange (SSE). Then we take the reduced sets which selected by the above four coefficients as input data for SVM. Compared with the forecasting results of SVM for the other three coefficients, the SVM with NDEM reduced data set has higher classification accuracy.