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Feature selection is an essential step in data mining. The core of it is to analyze and quantize the relevancy and redundancy between the features and the classes. In CFR feature selection method, they rarely consider which feature to choose if two or more features have the same value using evaluation criterion. In order to address this problem, the standard deviation is employed to adjust the importance between relevancy and redundancy. Based on this idea, a novel feature selection method named as Feature Selection Based on Weighted Conditional Mutual Information (WCFR) is introduced. Experimental results on ten datasets show that our proposed method has higher classification accuracy.

Higher education researchers are often challenged by the difficulty of empirically validating causal links posited by theories or inferred from correlational observations. The instrumental variable (IV) estimation strategy is one approach that researchers can use to estimate the causal impact of various higher education–related interventions. In this chapter, we discuss how the body of quantitative research specifically devoted to higher education has made use of the IV estimation strategy: we describe how this estimation strategy was used to address causality concerns and provide examples of the types of IVs that were used in various subfields of higher education research. Our discussion is based on a systematic review of a corpus of econometric studies on higher education–related issues that spans the last 30 years. The chapter concludes with a critical discussion of the use of IVs in quantitative higher education research and a discussion of good practices when using an IV estimation strategy.

The purpose of this paper evaluates the effects of the Great Western Development (GWD) policy on agricultural intensification, land use, agricultural production and rural poverty in western China.

The authors collect county-level data on land use, input application, grain crop production, income, poverty and geophysical characteristics for 1996–2005 and use a quasi-natural experimental design of difference-in-differences (DD) in the empirical analysis.

Results suggest that the GWD policy significantly increased the grain crop production in western China. This increase resulted from higher yield, with increased fertilizer use and agricultural electricity consumption per hectare, and more land allocated to grow grain crops. The policy also increased land-use concentration, reduced crop diversity and alleviated rural poverty in western China.

This paper makes three contributions. First, the authors add to the growing literature on the GWD policy by evaluating its effects on farm household decisions and exploring the mechanisms and broad socioeconomic impacts in western China. Second, the authors take advantage of a quasi-natural experimental design to improve the identification strategy where input use, land allocation, production and off-farm labor participation are all endogenous in a farm household. Third, the authors explore a long list of variables within one integrated dataset to present a comprehensive picture of the impact of the GWD policy.