When Partly Missing Data Matters in Software Effort Development Prediction

Abstract

The major objective of the paper is to investigate a new probabilistic supervised learning approach that incorporates “missingness” into a decision tree classifier splitting criterion at each particular attribute node in terms of software effort development predictive accuracy. The proposed approach is compared empirically with ten supervised learning methods (classifiers) that have mechanisms for dealing with missing values. 10 industrial datasets are utilized for this task. Overall, missing incorporated in attributes 3 is the top performing strategy, followed by C4.5, missing incorporated in attributes, missing incorporated in attributes 2, missing incorporated in attributes, linear discriminant analysis and so on. Classification and regression trees and C4.5 performed well in data with high correlations among attributes while k-nearest neighbour and support vector machines performed well in data with higher complexity (limited number of instances). The worst performing method is repeated incremental pruning to produce error reduction.