Classification problems have a large number of features in datasets, but not all them are useful for classification. Irrelevant and redundant features reduce the performance. These features may be considered as noisy. In order to solve this problem we perform a feature selection process. It is a preprocessing technique for solving classification problem. Feature Selection aims to choose relevan...

Selecting most representative features among a huge feature set is considered in this paper. One feature extraction and five feature selection algorithms are tested on features obtained form steel surfaces. Two new feature selection algorithms are introduced in this paper. Performance measure for feature selection algorithms is also introduced as a new method to select the best performing featu...

Project portfolio selection is very important subject of decision-makers in project-based organizations. The best assignment of resources to the most appropriate projects is necessary as financing projects with low benefit is just waste of organization's resources. However, existing project selection models pay not much attention the structure and special features of projects as a selection cri...

classification ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. this study aims to improve the results of identifying the persian handwritten letters using error correcting output coding (ecoc) ensemble method. furthermore, the feature selection is used to reduce the costs of ...

Phishing is one of the luring techniques used to exploit personal information. A phishing webpage detection system (PWDS) extracts features to determine whether it is a phishing webpage or not. Selecting appropriate features improves the performance of PWDS. Performance criteria are detection accuracy and system response time. The major time consumed by PWDS arises from feature extraction that ...

in this paper, a novel neuro-fuzzy based method combined with a feature selection technique is proposed for online dynamic voltage stability status prediction of power system. this technique uses synchronized phasors measured by phasor measurement units (pmus) in a wide-area measurement system. in order to minimize the number of neuro-fuzzy inputs, training time and complication of neuro-fuzzy ...

Using gray-scale texture features recently becomes a new trend in supervised machine learning crater detection algorithms. To provide better classification of craters in planetary images, feature subset selection is used to reduce irrelevant and redundant features. Feature selection is known to be NPhard. To provide an efficient suboptimal solution, three genetic algorithms are proposed to use ...

This paper developed a CAD (Computer Aided Diagnosis) system based on neural network and a proposed feature selection method. The proposed feature selection method is Maximum Difference Feature Selection (MDFS). Digital mammography is reliable method for early detection of breast cancer. The most important step in breast cancer diagnosis is feature selection. Computer automated feature selectio...

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