Software project management is one of the significant activates in the software development process. Software development effort estimation (SDEE) is a challenging task in the software project management. SDEE has been an old activity in computer industry from 1940s, and thus it has been reviewed for several times. A SDEE model is appropriate if it provides the accuracy and confidence simultaneously before a software project contract. Due to the uncertain nature of development estimates, and in order to increase the accuracy, researchers have recently focused on machine learning techniques. Choosing the most effective features to achieve higher accuracy in machine learning is crucial. In this work, for narrowing the semantic gap in SDEE, a hierarchical filter and wrapper feature selection (FS) techniques and fused measurement criteria are developed in a two-phase approach. In the first phase, the two-stage filter FS methods provide start sets for the wrapper FS techniques. In the second phase, a fused criterion is proposed to evaluate the accuracy in wrapper FS techniques. The experimental results show the validity and efficiency of the proposed approach for SDEE over a variety of standard datasets.
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