ISCAS OpenIR  > 互联网软件技术实验室
absent features or missing values?
Zhang Wen; Yang Ye; Wang Qing
2010
Conference Name22nd International Conference on Software Engineering and Knowledge Engineering, SEKE 2010
SourceSEKE 2010 - Proceedings of the 22nd International Conference on Software Engineering and Knowledge Engineering
Pages40705
Conference Date44013
Conference PlaceRedwood City, CA, United states
Indexed TypeEI
Publish PlaceUnited States
ISBN1891706268
Department(1) Laboratory for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
English AbstractTo clarify the essence of unobserved values of software effort dataset, we comparatively investigate the effectiveness of regarding the unobserved values as absent features and missing values with the task of predicting software effort. When regarding unobserved values as absent features, max-margin classification is used to classify the effort directly. While regarding unobserved values as missing values, we use different imputation methods, including MINI (mean imputation based k nearest neighbor hot-deck imputation), CMI (class mean imputation) and MI (mean imputation) to impute missing values firstly and then SVM (support vector machine) is used to classify software efforts. The experiments show that the treatment of regarding unobserved values in software effort dataset as missing values produces more desirable performance measured by accuracy in using historical data for software effort classification than regarding unobserved values as absent features. Moreover, among the mentioned three imputation methods, on CSBSG data set, CMI has better performance than MINI, and on ISBSG data set, MINI has better performance than CMI. We explain the outcome in this paper.
KeywordKnowledge Engineering Software Engineering
SponsorshipKnowledge Systems Institute Graduate School
Content Type会议论文
URIhttp://ir.iscas.ac.cn/handle/311060/8638
Collection互联网软件技术实验室
Recommended Citation
GB/T 7714
Zhang Wen,Yang Ye,Wang Qing. absent features or missing values?[C]. United States,2010:40705.
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