2012/11/12 by Hong Zhao, Zhao, Hong, Fan Min +3
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Mining Algorithms and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Rough Sets and Fuzzy Logic #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.1211.2512
This paper has been withdrawn by the author due to an error of the title
openalex publication_date 2012/11/12 · arxiv created 2013/06/03 · arxiv updated 2013/06/04 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
Minimal cost feature selection is devoted to obtain a trade-off between test costs and misclassification costs. This issue has been addressed recently on nominal data. In this paper, we consider numerical data with measurement errors and study minimal cost feature selection in this model. First, we build a data model with normal distribution measurement errors. Second, the neighborhood of each data item is constructed through the confidence interval. Comparing with discretized intervals, neighborhoods are more reasonable to maintain the information of data. Third, we define a new minimal total cost feature selection problem through considering the trade-off between test costs and misclassification costs. Fourth, we proposed a backtracking algorithm with three effective pruning techniques to deal with this problem. The algorithm is tested on four UCI data sets. Experimental results indicate that the pruning techniques are effective, and the algorithm is efficient for data sets with nearly one thousand objects.