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10.1109/IWSM-Mensura.2013.24guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
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The Effects of Variable Selection Methods on Linear Regression-Based Effort Estimation Models

Published: 23 October 2013 Publication History

Abstract

Stepwise regression has often been used for variable selection of effort estimation models. However it has been criticized for inappropriate selection, and another method is recommended. We thus examined the effects of Lasso, which is one of such variable selection methods. An experiment with datasets from PROMISE repository revealed that Lasso-based selection stably selected better variables than stepwise in predictive performance. We thus concluded Lasso-based selection is preferable to stepwise regression.

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cover image Guide Proceedings
IWSM-MENSURA '13: Proceedings of the 2013 Joint Conference of the 23nd International Workshop on Software Measurement (IWSM) and the 8th International Conference on Software Process and Product Measurement
October 2013
237 pages
ISBN:9780769550787

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IEEE Computer Society

United States

Publication History

Published: 23 October 2013

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  1. effort estimation
  2. lasso
  3. stepwise regression
  4. variable selection

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