Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/3845
Title: Feature subset selection for software cost modelling and estimation
Authors: Papatheocharous, Efi 
Papadopoulos, Harris
Andreou, Andreas S. ItemCrisRefDisplayStrategy.rp.deleted.icon
Keywords: Cost drivers;Cost estimations;Data sets;Empirical values;Feature selection methods;Feature subset selection;Fitting error;Modelling techniques;Pre-processing step;Project database;Software cost;Software cost models;Software development effort;Subset selection;Cost reduction;Estimation;Feature extraction;Set theory;Software engineering;Cost estimating
Issue Date: 2010
Publisher: CRL
Source: Engineering Intelligent Systems, 2010, Volume 18, Issue 3-4, Pages 233-246
Abstract: Feature selection has been recently used in the area of software engineering for improving the accuracy and robustness of software cost models. The idea behind selecting the most informative subset of features from a pool of available cost drivers stems from the hypothesis that reducing the dimensionality of datasets will significantly minimise the complexity and time required to reach to an estimation using a particular modelling technique. This work investigates the appropriateness of attributes, obtained from empirical project databases and aims to reduce the cost drivers used while preserving performance. Finding suitable subset selections that may cater improved predictions may be considered as a pre-processing step of a particular technique employed for cost estimation (filter or wrapper) or an internal (embedded) step to minimise the fitting error. This paper compares nine relatively popular feature selection methods and uses the empirical values of selected attributes recorded in the ISBSG and Desharnais datasets to estimate software development effort. 2010 CRL Publishing Ltd.
URI: http://ktisis.cut.ac.cy/handle/10488/3845
ISSN: 14728915
Rights: © 2010 CRL Publishing Ltd
Type: Article
Appears in Collections:Άρθρα/Articles

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