{"title":"A Comparative Study on Linear Combination Rules for Ensemble Effort Estimation","authors":"S. Amasaki","doi":"10.1109/SEAA.2017.11","DOIUrl":null,"url":null,"abstract":"Context: Software effort estimation is a critical factor for project success. A new approach called ensemble effort estimation gets popular because of its performance. While many combination rules have been proposed, they were only compared in a systematic literature review. Objective: To compare linear combination rules proposed in the past studies under the same condition based on empirical approach. Method: We conducted an experiment with 9 linear combination rules, 7 datasets, and 4 effort estimation models. Results: We found 6 out of 9 linear combination rules never underperformed its base learners. No linear combination rule was superior to the others. Conclusion: No definitive rule was found while some linear combination rules can give competitive or better estimates than its base learners.","PeriodicalId":151513,"journal":{"name":"2017 43rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 43rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SEAA.2017.11","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Context: Software effort estimation is a critical factor for project success. A new approach called ensemble effort estimation gets popular because of its performance. While many combination rules have been proposed, they were only compared in a systematic literature review. Objective: To compare linear combination rules proposed in the past studies under the same condition based on empirical approach. Method: We conducted an experiment with 9 linear combination rules, 7 datasets, and 4 effort estimation models. Results: We found 6 out of 9 linear combination rules never underperformed its base learners. No linear combination rule was superior to the others. Conclusion: No definitive rule was found while some linear combination rules can give competitive or better estimates than its base learners.