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K. Javed, H. A. Babri and M. Saeed, “Feature Selection Based on Class Dependent Densities for High-Dimen sional Binary Data,” IEEE Transactions on Knowledge and Data Engineering, Vol. 24, No. 3, 2012, pp. 465-477. doi:10.1109/TKDE.2010.263

has been cited by the following article:

• TITLE: Feature Selection for Time Series Modeling

  AUTHORS: Qing-Guo Wang, Xian Li, Qin Qin

  KEYWORDS: Time Series; Feature Selection; Correlation Analysis; Modeling; Nonlinear Systems

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.5 No.3, July 31, 2013

  ABSTRACT: In machine learning, selecting useful features and rejecting redundant features is the prerequisite for better modeling and prediction. In this paper, we first study representative feature selection methods based on correlation analysis, and demonstrate that they do not work well for time series though they can work well for static systems. Then, theoretical analysis for linear time series is carried out to show why they fail. Based on these observations, we propose a new correlation-based feature selection method. Our main idea is that the features highly correlated with progressive response while lowly correlated with other features should be selected, and for groups of selected features with similar residuals, the one with a smaller number of features should be selected. For linear and nonlinear time series, the proposed method yields high accuracy in both feature selection and feature rejection.