Add to ORKGAbstract. In this paper, we propose a novel filter for feature selection. Such filter relies on the estimation of the mutual information between features and classes. We bypass the estimation of the probability den-sity function with the aid of the entropic-graphs approximation of Rényi entropy, and the subsequent approximation of the Shannon one. The complexity of such bypassing process does not depend on the number of dimensions but on the number of patterns/samples, and thus the curse of dimensionality is circumvented. We show that it is then possible to outperform a greedy algorithm based on the maximal relevance and min-imal redundancy criterion. We successfully test our method both in the contexts of image classification and microarr...
Feature subset selection is an essential pre-processing task in machine learning and pattern recogni...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
The objective of the eliminating process is to reduce the size of the input feature set and at the s...
In this paper, we propose a novel filter for feature selection. Such filter relies on the estimation...
Along with the improvement of data acquisition techniques and the increasing computational capacity ...
International audienceWe consider the problem of feature selection, and we propose a new information...
International audienceWe consider the problem of feature selection, and we propose a new information...
Pattern recognition methods often deal with thousands of features. Therefore, dimensionality reducti...
Abstract—Feature selection problem has become the focus of much pattern classification research and ...
Pattern recognition methods often deal with samples consisting of thousands of features. Therefore, ...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
Measures of relevance between features play an important role in classification and regression analy...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
Feature subset selection is an essential pre-processing task in machine learning and pattern recogni...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
The objective of the eliminating process is to reduce the size of the input feature set and at the s...
In this paper, we propose a novel filter for feature selection. Such filter relies on the estimation...
Along with the improvement of data acquisition techniques and the increasing computational capacity ...
International audienceWe consider the problem of feature selection, and we propose a new information...
International audienceWe consider the problem of feature selection, and we propose a new information...
Pattern recognition methods often deal with thousands of features. Therefore, dimensionality reducti...
Abstract—Feature selection problem has become the focus of much pattern classification research and ...
Pattern recognition methods often deal with samples consisting of thousands of features. Therefore, ...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
Measures of relevance between features play an important role in classification and regression analy...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
Feature subset selection is an essential pre-processing task in machine learning and pattern recogni...
Data reduction is crucial in order to turn large datasets into information, the major purpose of dat...
The objective of the eliminating process is to reduce the size of the input feature set and at the s...