Add to ORKGIn this work the principle of homogeneity between labels and data clusters is exploited in order to develop a semi-supervised Feature Selection method. This principle permits the use of cluster information to improve the estimation of feature relevance in order to increase selection performance. Mutual Information is used in a Forward-Backward search process in order to evaluate the relevance of each feature to the data distribution and the existent labels, in a context of few labeled and many unlabeled instances
The objective of the eliminating process is to reduce the size of the input feature set and at the s...
The selection of features that are relevant for a prediction or classification problem is an importa...
Mutual information (MI) based approaches are a popu-lar feature selection paradigm. Although the sta...
This paper presents a new relevance index based on mutual information that is based on labeled and u...
In this paper, a supervised feature selection approach is presented, which is based on metric applie...
We propose a feature selection approach for clustering which extends Koller and Sahami's mutual...
As data acquisition has become relatively easy and inexpensive, data sets are becoming extremely lar...
Feature selection is an important step for data mining and machine learning. It can be used to reduc...
In previous work, we have shown that both unsupervised feature selection and the semi-supervised clu...
Feature selection is an important step for data mining and machine learning to deal with the curse o...
Clustering is part of data mining where data mining is a process in which it is used to analyze data...
This paper introduces concepts and algorithms of feature selection, surveys existing feature selecti...
One of the challenges in data mining is the dimensionality of data, which is often very high and pre...
International audienceSimultaneous selection of the number of clusters and of a relevant subset of f...
Along with the improvement of data acquisition techniques and the increasing computational capacity ...
The objective of the eliminating process is to reduce the size of the input feature set and at the s...
The selection of features that are relevant for a prediction or classification problem is an importa...
Mutual information (MI) based approaches are a popu-lar feature selection paradigm. Although the sta...
This paper presents a new relevance index based on mutual information that is based on labeled and u...
In this paper, a supervised feature selection approach is presented, which is based on metric applie...
We propose a feature selection approach for clustering which extends Koller and Sahami's mutual...
As data acquisition has become relatively easy and inexpensive, data sets are becoming extremely lar...
Feature selection is an important step for data mining and machine learning. It can be used to reduc...
In previous work, we have shown that both unsupervised feature selection and the semi-supervised clu...
Feature selection is an important step for data mining and machine learning to deal with the curse o...
Clustering is part of data mining where data mining is a process in which it is used to analyze data...
This paper introduces concepts and algorithms of feature selection, surveys existing feature selecti...
One of the challenges in data mining is the dimensionality of data, which is often very high and pre...
International audienceSimultaneous selection of the number of clusters and of a relevant subset of f...
Along with the improvement of data acquisition techniques and the increasing computational capacity ...
The objective of the eliminating process is to reduce the size of the input feature set and at the s...
The selection of features that are relevant for a prediction or classification problem is an importa...
Mutual information (MI) based approaches are a popu-lar feature selection paradigm. Although the sta...