A feature selection model for binary classification of imbalanced data based on preference for target instances

Abstract

Telemarketers of online job advertising firms face significant challenges understanding the advertising demands of small-sized enterprises. The effective use of data mining approach can offer e-recruitment companies an improved understanding of customers' patterns and greater insights of purchasing trends. However, prior studies on classifier built by data mining approach provided limited insights into the customer targeting problem of job advertising companies. In this paper we develop a single feature evaluator and propose an approach to select a desired feature subset by setting a threshold. The proposed feature evaluator demonstrates its stability and outstanding performance through empirical experiments in which real-world customer data of an e-recruitment firm are used. Practically, the findings together with the model may help telemarketers to better understand their customers. Theoretically, this paper extends existing research on feature selection for binary classification of imbalanced data.