handling missing data in software effort prediction with naive bayes and em algorithm

ISCAS OpenIR

	handling missing data in software effort prediction with naive bayes and em algorithm
	Zhang Wen; Yang Ye; Wang Qing
	2011
会议名称	7th International Conference on Predictive Models in Software Engineering, PROMISE 2011, Co-located with ESEM 2011
会议录名称	ACM International Conference Proceeding Series
页码	-
会议日期	September
会议地点	Banff, AB, Canada
收录类别	EI
ISBN	9781450307093
部门归属	(1) Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences Beijing 100190 China
摘要	Background: Missing data, which usually appears in software effort datasets, is becoming an important problem in software effort prediction. Aims: In this paper, we adapt nai¨Bayes and EM (Expectation Maximization) for software effort prediction, and develop two embedded strategies: missing data toleration and missing data imputation, to handle the missing data in software effort datasets. Method: The missing data toleration strategy ignores missing values in software effort datasets while missing data imputation strategy uses observed values to impute missing values. Results: Experiments on ISBSG and CSBSG datasets demonstrate that: 1)both proposed strategies outperform BPNN with classic imputation techniques as MI and MINI. Meanwhile, the imputation strategy outperforms toleration strategy in most cases and has produced the highest accuracy as 75.15%; 2) the unlabeled projects used in training prediction model has signifintly improved the performances of effort prediction of nai¨Bayes and EM with both strategies, especially when the size of training data to the size of unlabeled data is at a relatively optimal level; 3) each class of software effort data exactly corresponds to a Gaussian component for both ISBSG and CSBSG datasets. Conclusion: Although initial experiments on ISBSG data set demonstrate some promising aspects of the proposed strategies, we cannot draw that they can be generalized to be applied in all the other software effort datasets. Copyright © 2011 ACM.; Background: Missing data, which usually appears in software effort datasets, is becoming an important problem in software effort prediction. Aims: In this paper, we adapt nai¨Bayes and EM (Expectation Maximization) for software effort prediction, and develop two embedded strategies: missing data toleration and missing data imputation, to handle the missing data in software effort datasets. Method: The missing data toleration strategy ignores missing values in software effort datasets while missing data imputation strategy uses observed values to impute missing values. Results: Experiments on ISBSG and CSBSG datasets demonstrate that: 1)both proposed strategies outperform BPNN with classic imputation techniques as MI and MINI. Meanwhile, the imputation strategy outperforms toleration strategy in most cases and has produced the highest accuracy as 75.15%; 2) the unlabeled projects used in training prediction model has signifintly improved the performances of effort prediction of nai¨Bayes and EM with both strategies, especially when the size of training data to the size of unlabeled data is at a relatively optimal level; 3) each class of software effort data exactly corresponds to a Gaussian component for both ISBSG and CSBSG datasets. Conclusion: Although initial experiments on ISBSG data set demonstrate some promising aspects of the proposed strategies, we cannot draw that they can be generalized to be applied in all the other software effort datasets. Copyright © 2011 ACM.
关键词	Algorithms Data Handling Embedded Software Experiments Forecasting Mathematical Models Models Predictive Control Systems Software Engineering
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16211
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Zhang Wen,Yang Ye,Wang Qing. handling missing data in software effort prediction with naive bayes and em algorithm[C],2011:-.