Add to ORKGDimensionality reduction of the problem space through detection and removal of variables, contributing little or not at all to classification, is able to relieve the computational load and instance acquisition effort, considering all the data attributes accessed each time around. The approach to feature selection in this paper is based on the concept of coherent accumulation of data about class centers with respect to coordinates of informative features. Ranking is done on the degree to which different variables exhibit random characteristics. The results are being verified using the Nearest Neighbor classifier. This also helps to address the feature irrelevance and redundancy, what ranking does not immediately decide. Additionally, feature...
We give a brief overview of feature selection methods used in statistical classification. We cover f...
One major component of machine learning is feature analysis which comprises of mainly two processes:...
The amount of information in the form of features and variables avail-able to machine learning algor...
AbstractFeature selection, as a dimensionality reduction technique, aims to choosing a small subset ...
In machine learning the classification task is normally known as supervised learning. In supervised ...
Data dimensionality is growing exponentially, which poses chal-lenges to the vast majority of existi...
AbstractBefore a pattern classifier can be properly designed, it is necessary to consider the featur...
summary:The purpose of feature selection in machine learning is at least two-fold - saving measureme...
1 Introduction The process of feature selection, also known as attribute subset selection is a key f...
AbstractFeature selection is an effective technique in dealing with dimensionality reduction. For cl...
Feature selection is a term standardin data mining to reduce inputs to a manageable size for analysi...
Feature selection is a term standardin data mining to reduce inputs to a manageable size for analysi...
Machine learning is used nowadays to build models for classification and regression tasks, among oth...
Feature selection is an important data pre-processing step that comes before applying a machine lear...
Classification of data crosses different domains has been extensively researched and is one of the b...
We give a brief overview of feature selection methods used in statistical classification. We cover f...
One major component of machine learning is feature analysis which comprises of mainly two processes:...
The amount of information in the form of features and variables avail-able to machine learning algor...
AbstractFeature selection, as a dimensionality reduction technique, aims to choosing a small subset ...
In machine learning the classification task is normally known as supervised learning. In supervised ...
Data dimensionality is growing exponentially, which poses chal-lenges to the vast majority of existi...
AbstractBefore a pattern classifier can be properly designed, it is necessary to consider the featur...
summary:The purpose of feature selection in machine learning is at least two-fold - saving measureme...
1 Introduction The process of feature selection, also known as attribute subset selection is a key f...
AbstractFeature selection is an effective technique in dealing with dimensionality reduction. For cl...
Feature selection is a term standardin data mining to reduce inputs to a manageable size for analysi...
Feature selection is a term standardin data mining to reduce inputs to a manageable size for analysi...
Machine learning is used nowadays to build models for classification and regression tasks, among oth...
Feature selection is an important data pre-processing step that comes before applying a machine lear...
Classification of data crosses different domains has been extensively researched and is one of the b...
We give a brief overview of feature selection methods used in statistical classification. We cover f...
One major component of machine learning is feature analysis which comprises of mainly two processes:...
The amount of information in the form of features and variables avail-able to machine learning algor...