The main purpose of feature subset selection is to remove irrelevant and redundant features from data, so that learning algorithms can be trained by a subset of relevant features. So far, many algorithms have been developed for the feature subset selection, and most of these algorithms suffer from two major problems in solving high-dimensional datasets: First, some of these algorithms search in...

Feature selection means finding most useful features and it will produce suitable results among entire set of features. An algorithm is used to selecting a feature and it may be evaluated from both efficiency and effectiveness point of view. Efficiency is related to the time required to find a subset of features while the effectiveness is related to quality of subset of features. Based on these...

In this paper, we propose a new feature subset evaluation method for feature selection in object tracking. According to the fact that a feature which is useless by itself could become a good one when it is used together with some other features, we propose to evaluate feature subsets as a whole for object tracking instead of scoring each feature individually and find out the most distinguishabl...

Feature Selection is the process of selecting the momentous feature subset from the original ones. This technique is frequently used as a preprocessing technique in data mining. In this study, a new feature selection algorithm is proposed and is called Modified Fisher Score Principal Feature Analysis (MFSPFA). The new algorithm is developed by combining the proposed Modified Fisher Score (MFS) ...

Most of the conventional feature selection algorithms have a drawback whereby a weakly ranked gene that could perform well in terms of classification accuracy with an appropriate subset of genes will be left out of the selection. Considering this shortcoming, we propose a feature selection algorithm in gene expression data analysis of sample classifications. The proposed algorithm first divides...

The goal of feature selection is to find the optimal subset consisting of m features chosen from the total n features. One critical problem for many feature selection methods is that an exhaustive search strategy has to be applied to seek the best subset among all the possible ( n m ) feature subsets, which usually results in a considerably high computational complexity. The alternative subopti...

Objective(s): This study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets. Materials and Methods: To evaluate effectiveness of proposed feature selection method, we ...

OBJECTIVE Many applications such as biomedical signals require selecting a subset of the input features in order to represent the whole set of features. A feature selection algorithm has recently been proposed as a new approach for feature subset selection. METHODS Feature selection process using ant colony optimization (ACO) for 6 channel pre-treatment electroencephalogram (EEG) data from th...

CBIR applies to techniques for retrieving similar images from image databases, based on automated feature selection methods. Feature selection is an important topic in data mining, especially for high dimensional datasets. Feature selection (also known as subset selection) is a process commonly used in machine learning, wherein subsets of the features available from the data are selected for ap...