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 ...

Feature selection is used to improve performance of learning algorithms by finding a minimal subset of relevant features. Since the process of feature selection is computationally intensive, a trade-off between the quality of the selected subset and the computation time is required. In this paper, we are presenting a novel, anytime algorithm for feature selection, which gradually improves the q...

Feature Selection techniques usually follow some search stra tegy to select a suitable subset from a set of features Most neural network growing algorithms perform a search with Forward Selection with the ob jective of nding a reasonably good subset of neurons Using this link be tween both elds feature selection and neuron selection we propose and analyze di erent algorithms for the constructio...

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...

This paper proposes two wrapper based feature selection approaches, which are single feature ranking and binary particle swarm optimisation (BPSO) based feature subset ranking. In the first approach, individual features are ranked according to the classification accuracy so that feature selection can be accomplished by using only a few top-ranked features for classification. In the second appro...

In this paper, we propose a new feature evaluation method that forms the basis for feature ranking and feature selection. The method starts by generating a number of feature subsets in a random fashion and evaluates features based on the derived subsets. It then proceeds in a number of stages. In each stage, it inputs the features whose ranks in the previous stage were above the median rank and...

Feature selection is an important task in pattern recognition. Support Vector Machine (SVM) and Minimax Probability Machine (MPM) have been successfully used as the 15 classification framework for feature selection. However, these paradigms cannot automatically control the balance between prediction accuracy and the number of selected 17 features. In addition, the selected feature subsets are a...