Titlebook: Optimization Based Data Mining: Theory and Applications; Yong Shi,Yingjie Tian,Jianping Li Book 2011 Springer-Verlag London Limited 2011 C

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書目名稱Optimization Based Data Mining: Theory and Applications讀者反饋學(xué)科排名

定點(diǎn)

靈敏

1610-3947 re the two main components of data mining.Constructs SVM‘s f.Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Prog

Institution

殘忍

Robust Support Vector Machinese is involved. Then, we also establish a multi-class algorithm based on the above robust SVORM for general multi-class classification problem with perturbations. Furthermore, we construct a robust unsupervised and semi-supervised SVC for the problems with uncertainty information.

heterodox

Multiple Criteria Linear Programmingrogramming. Then MCLP models for multiple classes and unbalanced training set are constructed separately. Furthermore, in order to ensure the existence of solution, we add regularization terms in the objective function of MCLP, and constructed regularized MCLP (RMCLP) model.

你敢命令

Non-additive MCLPure attributes with respect to the non-additive measure. The non-additive MCLP classification models are constructed in this chapter, and because the using of non-additive measure increases the computational cost, two major solutions to reduce the number of non-additive measures are given: hierarchical Choquet integral and the K-additive measure.

CRACK

Firm Financial Analysisnt different problems in finance and banking. For comparison purpose, the result of MCQP is compared with four well-know classification methods: SPSS, linear discriminant analysis (LDA), Decision Tree based See5, SVMlight, and LibSVM.

保存

Personal Credit Managementhe MCQP classification, then compare the performance of MCQP with MCLP, linear discriminant analysis (LDA), decision tree (DT), support vector machine (SVM), and neural network (NN) methods in terms of predictive accuracy.

osculate

Support Vector Machines for Classification Problemsandard algorithm .-support vector classification (.-SVC). Especially, considering the classification problem of which the training set with nominal attributes, we built a new SVM which can learn the distance of the nominal attribute values, to improve most popular approaches assuming that all attribute values are of equal distance from each other.