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文章标题;Cost-Sensitive learning on Classification,  作者; qin yang;
刊物;COMPUTER MODELLING & NEW TECHNOLOGIES 2014
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Accession number:        

20150500473800
          Title:         Cost-Sensitive learning on classification
          Authors:         Yang, Qin1 Email author yangqin@sicau.edu.cn; Zhou, Changyao2
          Author affiliation:         1 School of Business, Sichuan Agricultural University, Dujiangyan, Sichuan, China
                  2 School of Resources and environment, Sichuan Agricultural University, Wenjiang, Sichuan, China
          Corresponding author:         Yang, Qin
          Source title:         Computer Modelling and New Technologies
          Abbreviated source title:         Comput. Model. New Technol.
          Volume:         18
          Issue:         9
          Issue date:         2014
          Publication year:         2014
          Pages:         380-386
          Language:         English
          ISSN:         14075806
          E-ISSN:         14075814
          Document type:         Journal article (JA)
          Publisher:         Transport and Telecommunication Institute, Lomonosova street 1, Riga, LV-1019, Latvia
          Abstract:         Real-world predictive data mining (classification or regression) problems are often cost sensitive, meaning that different types of prediction errors are not equally costly. In this paper we propose a new algorithm for cost-sensitive classification in a multiple time series prediction problems. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification errors. The proposed model is evaluated in a real world application based on a network of satellite network map distributed in land spatial pattern evolution in Chengdu Plain. These satellite networks generate multiple time series data representing land spatial pattern. This study presents a new algorithm for cost-sensitive classification that deal with class imbalance using both recompiling and CSL. The method combines and compares several sampling methods with CSL using support vector machines (SVM). We build our cost-benefit model for the prediction process as a function of satellite network in a distributed land spatial and measured the optimum number of satellite network that will balance the expenses of the system with the prediction accuracy.
          Number of references:         19
          Main heading:         Cost benefit analysis
          Controlled terms:         Cost effectiveness  -  Costs  -  Data mining  -  Decision trees  -  Errors  -  Forecasting  -  Genetic algorithms  -  Satellites  -  Support vector machines  -  Time series  -  Trees (mathematics)
          Uncontrolled terms:         Classification errors  -  Cost sensitive classifications  -  Cost-benefit models  -  Cost-sensitive learning  -  Distributed satellites  -  Multiple time series  -  Prediction accuracy  -  Predictive data mining
          Classification code:         655.2 Satellites -  723 Computer Software, Data Handling and Applications -  911 Cost and Value Engineering; Industrial Economics -  911.2 Industrial Economics -  921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory -  922.2 Mathematical Statistics
          Database:         Compendex
                  Compilation and indexing terms, © 2015 Elsevier Inc.
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