Journal ArticleDOI
Unsupervised feature selection using feature similarity
TLDR
An unsupervised feature selection algorithm suitable for data sets, large in both dimension and size, based on measuring similarity between features whereby redundancy therein is removed, which does not need any search and is fast.Abstract:
In this article, we describe an unsupervised feature selection algorithm suitable for data sets, large in both dimension and size. The method is based on measuring similarity between features whereby redundancy therein is removed. This does not need any search and, therefore, is fast. A new feature similarity measure, called maximum information compression index, is introduced. The algorithm is generic in nature and has the capability of multiscale representation of data sets. The superiority of the algorithm, in terms of speed and performance, is established extensively over various real-life data sets of different sizes and dimensions. It is also demonstrated how redundancy and information loss in feature selection can be quantified with an entropy measure.read more
Citations
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Journal ArticleDOI
A survey on feature selection methods
Girish Chandrashekar,Ferat Sahin +1 more
TL;DR: The objective is to provide a generic introduction to variable elimination which can be applied to a wide array of machine learning problems and focus on Filter, Wrapper and Embedded methods.
Journal ArticleDOI
Image retrieval: Ideas, influences, and trends of the new age
TL;DR: Almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation are surveyed, and the spawning of related subfields are discussed, to discuss the adaptation of existing image retrieval techniques to build systems that can be useful in the real world.
Journal ArticleDOI
Toward integrating feature selection algorithms for classification and clustering
TL;DR: With the categorizing framework, the efforts toward-building an integrated system for intelligent feature selection are continued, and an illustrative example is presented to show how existing feature selection algorithms can be integrated into a meta algorithm that can take advantage of individual algorithms.
Proceedings Article
Feature selection for high-dimensional data: a fast correlation-based filter solution
TL;DR: A novel concept, predominant correlation, is introduced, and a fast filter method is proposed which can identify relevant features as well as redundancy among relevant features without pairwise correlation analysis.
Journal Article
Efficient Feature Selection via Analysis of Relevance and Redundancy
TL;DR: It is shown that feature relevance alone is insufficient for efficient feature selection of high-dimensional data, and a new framework is introduced that decouples relevance analysis and redundancy analysis.
References
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Journal ArticleDOI
Linear Statistical Inference and Its Applications.
Journal ArticleDOI
Floating search methods in feature selection
TL;DR: Sequential search methods characterized by a dynamically changing number of features included or eliminated at each step, henceforth "floating" methods, are presented and are shown to give very good results and to be computationally more effective than the branch and bound method.
Book ChapterDOI
A Practical Approach to Feature Selection
Kenji Kira,Larry A. Rendell +1 more
TL;DR: Comparison with other feature selection algorithms shows Relief's advantages in terms of learning time and the accuracy of the learned concept, suggesting Relief's practicality.
Book ChapterDOI
Estimating attributes: analysis and extensions of RELIEF
TL;DR: In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies among them and is analysed and extended to deal with noisy, incomplete, and multi-class data sets.