Mutual information based feature selection for symbolic interval data

Liu, Qin; Jing, Wang; Xiao, Jiakai; Zhu, Hongming

doi:10.1049/cp.2014.1537

Cited by 2 publications

(2 citation statements)

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“…The best MMRE results are obtained for k =1 . This result is also obtained in (Auer et al 2006;Chiu and Huang 2007;Q. Liu et al 2014).…”

Section: Influence Of K Value In the Accuracy Of The Algorithmssupporting

confidence: 77%

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Application of mutual information-based sequential feature selection to ISBSG mixed data

Fernández-Diego

2017

Software Qual J

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There is still little research work focused on Feature Selection (FS) techniques including both categorical and continuous features in Software Development Effort Estimation (SDEE) literature. This paper addresses the problem of selecting the most relevant features from ISBSG (International Software Benchmarking Standards Group) dataset to be used in SDEE. The aim is to show the usefulness of splitting the ranked list of features provided by a mutual information-based sequential FS approach in two, regarding categorical and continuous features. These lists are later recombined according to the accuracy of a case-based reasoning model. Thus, four FS algorithms are compared using a complete dataset with 621 projects and 12 features from ISBSG. On the one hand, two algorithms just consider the relevance, while the remaining two follow the criterion of maximizing relevance and also minimizing redundancy between any independent feature and the already selected features. On the other hand, the algorithms that do not discriminate between continuous and categorical features consider just one list, whereas those that differentiate them use two lists that are later combined. As a result, the algorithms that use two lists present better performance than those algorithms that use one list. Thus, it is meaningful to consider two different lists of features so that the categorical features may be selected more frequently. We also suggest promoting the usage of Application Group, Project Elapsed Time, and First Data Base System features with preference over the more frequently used Development Type, Language Type, and Development Platform.

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“…The best MMRE results are obtained for k =1 . This result is also obtained in (Auer et al 2006;Chiu and Huang 2007;Q. Liu et al 2014).…”

Section: Influence Of K Value In the Accuracy Of The Algorithmssupporting

confidence: 77%

“…FS aims to choose a subset of the original features according to a selection criterion, so that the redundancy and noise in the original feature set is minimized (Q. Liu et al 2014). On the one hand, by eliminating irrelevant features, the remaining ones are useful for estimation purposes (Dejaeger et al 2012).…”

Section: Introductionmentioning

confidence: 99%