On the value of filter feature selection techniques in homogeneous ensembles effort estimation

TitreOn the value of filter feature selection techniques in homogeneous ensembles effort estimation
Publication TypeJournal Article
Year of Publication2021
AuthorsHosni, M, Idri, A, Abran, A
JournalJournal of Software: Evolution and Process
Volume33
Mots-clésDecision trees, Effort Estimation, Feature extraction, K-nearest neighbors, Multilayer neural networks, Nearest neighbor search, Object oriented programming, Predictive capabilities, Project management, Random processes, Random subspace method, Selection techniques, Software design, Software development effort, Software project management, Software systems, Support vector regression
Abstract

Software development effort estimation (SDEE) remains as the principal activity in software project management planning. Over the past four decades, several methods have been proposed to estimate the effort required to develop a software system, including more recently machine learning (ML) techniques. Because ML performance accuracy depends on the features that feed the ML technique, selecting the appropriate features in the preprocessing data step is important. This paper investigates three filter feature selection techniques to check the predictive capability of four single ML techniques: K-nearest neighbor, support vector regression, multilayer perceptron, and decision trees and their homogeneous ensembles over six well-known datasets. Furthermore, the single and ensembles techniques were optimized using the grid search optimization method. The results suggest that the three filter feature selection techniques investigated improve the reasonability and the accuracy performance of the four single techniques. Moreover, the homogeneous ensembles are statistically more accurate than the single techniques. Finally, adopting a random process (i.e., random subspace method) to select the inputs feature for ML technique is not always effective to generate an accurate homogeneous ensemble. © 2021 John Wiley & Sons, Ltd.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85103402056&doi=10.1002%2fsmr.2343&partnerID=40&md5=0f4c04b547f3d628d6db8d65b74912e5
DOI10.1002/smr.2343
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