When Partly Missing Data Matters in Software Effort Development Prediction
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- Twala Bhekisipho
- Department of Electrical and Electronic Engineering Science, University of Johannesburg
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説明
<p>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.</p>
収録刊行物
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- Journal of Advanced Computational Intelligence and Intelligent Informatics
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Journal of Advanced Computational Intelligence and Intelligent Informatics 21 (5), 803-812, 2017-09-20
富士技術出版株式会社
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詳細情報 詳細情報について
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- CRID
- 1390282763068012288
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- NII論文ID
- 130007520206
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- NII書誌ID
- AA12042502
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- ISSN
- 18838014
- 13430130
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- NDL書誌ID
- 028510751
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可