In many pattern recognition applications, high-dimensional feature vectors impose a high computational cost as well as the risk of “overfitting”. Feature Selection addresses the dimensionality reduction problem by determining a subset of available features which is most essential for classification. This paper presents a novel feature selection method named filtered and supported sequential for...

This chapter introduces a neural network based approach for the identification of human affective state in speech signals. A group of potential features are first identified and extracted to represent the characteristics of different emotions. To reduce the dimensionality of the feature space, whilst increasing the discriminatory power of the features, a systematic feature selection approach wh...

1. Language Independent Recognition of Human Emotion using Artificial Neural Networks Waqas Bhatti, The University of Sydney, Australia Yongjin Wang, University of Toronto, Canada Ling Guan, Ryerson University,Canada This article presents a language-independent emotion recognition system for the identification of human affective state in the speech signal. A group of potential features are firs...

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...

A major difficulty of text categorization is the high dimensionality of the original feature space. Feature selection plays an important role in text categorization. Automatic feature selection methods such as document frequency thresholding (DF), information gain (IG), mutual information (MI), and so on are commonly applied in text categorization. Many existing experiments show IG is one of th...

Background: In the current study, a hybrid feature selection approach involving filter and wrapper methods is applied to some bioscience databases with various records, attributes and classes; hence, this strategy enjoys the advantages of both methods such as fast execution, generality, and accuracy. The purpose is diagnosing of the disease status and estimating of the patient survival. Method...

objective: diabetes is one of the most common metabolic diseases. earlier diagnosis of diabetes and treatment of hyperglycemia and related metabolic abnormalities is of vital importance. diagnosis of diabetes via proper interpretation of the diabetes data is an important classification problem. classification systems help the clinicians to predict the risk factors that cause the diabetes or pre...

In this paper, a new wrapper method for feature selection, namely IAFN-FS (Incremental ANalysis Of VAriance and Functional Networks for Feature Selection) is presented. The method uses as induction algorithm the AFN (ANOVA and Functional Networks) learning method; follows a backward non-sequential strategy from the complete set of features (thus allowing to discard several variables in one step...