Feature selection plays an important role in pattern classification. In this paper, we present an improved Branch and Bound algorithm for optimal feature subset selection. This algorithm searches for an optimal solution in a large solution tree in an efficient manner by cutting unnecessary paths which are guaranteed not to contain the optimal solution. Our experimental results demonstrate the e...

We propose a sequential forward feature selection method to find a subset of features that are most relevant to the classification task. Our approach uses novel estimation of the conditional mutual information between candidate feature and classes, given a subset of already selected features which is utilized as a classifier independent criterion for evaluation of feature subsets. The proposed ...

Feature selection involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. While the efficiency concerns the time required to find a subset of features, the effectiveness is related to the quality of the subset of fea...

This paper presents an evolutionary algorithm based technique to solve multi-objective feature subset selection problem. The data used for classification contains large number of features called attributes. Some of these attributes are not relevant and needs to be eliminated. In classification procedure, each feature has an effect on the accuracy, cost and learning time of the classifier. So, t...

Fisher score and Laplacian score are two popular feature selection algorithms, both of which belong to the general graph-based feature selection framework. In this framework, a feature subset is selected based on the corresponding score (subset-level score), which is calculated in a trace ratio form. Since the number of all possible feature subsets is very huge, it is often prohibitively expens...

This study shows how artificial neural networks can be used to model consumer choice. Our study focuses on two key issues in neural network modeling— model building and feature selection. Using the cross-validation approach, we address these two issues together and specifically examine the effectiveness of a backward feature selection algorithm for consumer situational choices of communication ...

This study shows how artificial neural networks can be used to model consumer choice. Our study focuses on two key issues in neural network modeling – model and feature selection. Using the cross-validation approach, we address these two issues together and specifically examine the effectiveness of a backward feature selection algorithm for consumer situational choices of communication modes. R...

Feature selection (also known as subset selection) is a process commonly used in machine learning, wherein a subset of the features available from the data are selected for application of a learning algorithm. The best subset contains the least number of dimensions that most contribute to accuracy; we discard the remaining, unimportant dimensions. This is an important stage of preprocessing and...

Feature selection is always an important and difficult issue in pattern recognition, machine learning and data mining. In this paper, a novel approach called resemblance coefficient feature selection (RCFS) is proposed. Definition, properties of resemblance coefficient (RC) and the evaluation criterion of the optimal feature subset are given firstly. Feature selection algorithm using RC criteri...

The aim of this paper is to develop a hybrid metaheuristic based on Variable Neighbourhood Search and Tabu Search for solving the Feature Subset Selection Problem in classification. Given a set of instances characterized by several features, the classification problem consists of assigning a class to each instance. Feature Subset Selection Problem selects a relevant subset of features from the ...