Accurate and Stable Feature Selection Powered by Iterative Backward Selection and Cumulative Ranking Score of Features
نویسندگان
چکیده
منابع مشابه
Stable and Accurate Feature Selection
In addition to accuracy, stability, having not too significant changes in the selected features when the identity of samples change, is also a measure of success for a feature selection algorithm. Stability could especially be a concern when the number of samples in a data set is small and the dimensionality is high. In this study, we introduce a stability measure that can be used for the case ...
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This chapter describes a method of feature selection and ranking based on human expert knowledge and training and testing of a neural network. Being computationally efficient, the method is less sensitive to round-off errors and noise in the data than the traditional methods of feature selection and ranking grounded on the sensitivity analysis. The method may lead to a significant reduction of ...
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Many feature selection and feature ranking methods have been proposed. Using real and artificial data an attempt has been made to compare some of these methods. The "feature relevance index" used seems to have little effect on the relative ranking. For continuous features discretization and kernel smoothing are compared. Selection of subsets of features using hashing techniques is compared with...
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Many indices for evaluation of features have been considered. Applied to single features they allow for filtering irrelevant attributes. Algorithms for selection of subsets of features also remove redundant features. Hashing techniques enable efficient application of feature relevance indices to selection of feature subsets. A number of such methods have been applied to artificial and real-worl...
متن کاملModified backward feature selection by cross validation
Since training of a classifier takes time, usually some criterion other than the recognition rate is used for feature selection. This may, however, leads to deteriorating the generalization ability by feature selection. To overcome this problem, in this paper, we propose modified backward feature selection by cross validation. Initially, we determine the candidate set which consists of the feat...
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ژورنال
عنوان ژورنال: Indian Journal of Science and Technology
سال: 2015
ISSN: 0974-5645,0974-6846
DOI: 10.17485/ijst/2015/v8i11/71766