A novel reconstructed training-set SVM with roulette cooperative coevolution for financial time series classification. (1st June 2019)

• Record Type: Journal Article
• Authors: Chao, Luo; Zhipeng, Jiang; Yuanjie, Zheng
• Is Part Of: Expert systems with applications. Volume 123(2019)
• Page Start: 283
• Page End: 298
• Publication Date: 2019-06-01
• Subjects: Reconstructed training-set SVM -- Cooperative coevolution -- Time series -- Classification; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.01.022
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel SVM is proposed for high noise and unbalanced distribution data. Feature selection is improved by a novel method using the hierarchical relations in feature sets. Roulette algorithm is introduced into cooperative coevolution. Abstract: In real applications, noises are often present in the obtained data, which would considerably affect the performance of machine learning models. Although support vector machine (SVM) is a classic and efficient learning model, however, it is sensitive to noises in the training data. In this paper, a novel support vector machine named as reconstructed training-set SVM (RTS-SVM) is proposed to implement classification for high-noise data, where the roulette cooperative coevolution algorithm (R-CC) is used to optimize the parameters of RTS-SVM. The proposed SVM model is applicable to make the classification of high-noise data by tackling with the sensitive effect of the ``soft margin'' of SVM on the original training set. By means of the hierarchical relations existing in feature sets, hierarchical grouping (HG) algorithm is applied to construct feature subsets, based on which R-CC coordinates the parameters of RTS-SVM to achieve the optimization of the whole model. The application of the proposed scheme in the classification of financial time series is mainly discussed. Besides, the proposed model is also verified by using synthetic data with high noises and daily life data sets. Examples are provided to illustrate the effectiveness and practicability of the proposed algorithm.