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On the value of filter feature selection techniques in homogeneous ensembles effort estimation
Author(s) -
Hosni Mohamed,
Idri Ali,
Abran Alain
Publication year - 2021
Publication title -
journal of software: evolution and process
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 29
eISSN - 2047-7481
pISSN - 2047-7473
DOI - 10.1002/smr.2343
Subject(s) - computer science , feature selection , preprocessor , artificial intelligence , data mining , software , filter (signal processing) , support vector machine , feature (linguistics) , pattern recognition (psychology) , multilayer perceptron , machine learning , subspace topology , random forest , k nearest neighbors algorithm , dimensionality reduction , process (computing) , curse of dimensionality , homogeneous , artificial neural network , mathematics , linguistics , philosophy , combinatorics , computer vision , programming language , operating system
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.