This paper describes a similar speaker selection technique based on distance metric learning. Our aim is selection of a perceptually similar speaker using acoustic features from a multispeaker database. A novel point of the proposed technique is training a transform matrix using the perceptual voice quality similarity between many speakers obtained from a subjective evaluation to convert acoust...

Data mining is a form of knowledge discovery essential for solving problems in a specific domain. Classification is a technique used for discovering classes of unknown data. Various methods for classification exists like bayesian, decision trees, rule based, neural networks etc. Before applying any mining technique, irrelevant attributes needs to be filtered. Filtering is done using different f...

Feature selection has proven to be a valuable technique in supervised learning for improving predictive accuracy while reducing the number of attributes considered in a task. We investigate the potential for similar benefits in an unsupervised learning task, conceptual clustering. The issues raised in feature selection by the absence of class labels are discussed and an implementation of a sequ...

The application of feature selection techniques greatly reduces the computational cost of classifying highdimensional data. Feature selection algorithms of varying performance and computational complexities have been studied previously. This paper compares the performance of classical sequential methods, a floating search method, and the “globally optimal” branch and bound algorithm when applie...

Clustering is a widely studied problem in data mining. Ai techniques, evolutionary techniques and optimization techniques are applied to this field. In this study, a novel hybrid modeling approach proposed for clustering and feature selection. Ant colony clustering technique is used to segment breast cancer data set. To remove irrelevant or redundant features from data set for clustering Sequen...

An intrusion detection system refers to the systems which detect the security breaches in a network. The main goal of IDS is to detect the attacks and raise alarms if an attack is found. These IDS has limitation of raising false alarms and this limitation depends on improper classification of attacks due to high dimensionality of data on a network. Improper classification is due to improper fea...

Introduction: Application of biofeedback techniques in rehabilitation has turned into an exciting research area during the recent decade. Providing an appropriate visual or auditory biofeedback signal is the most critical requirement of a biofeedback technique. In this regard, changes in Surface Electromyography (SEMG) signals during wrist movement can be used to generate an indictable visual b...

Abstract Introduction: Myocardial Infarction, also known as heart attack, normally occurs due to such causes as smoking, family history, diabetes, and so on. It is recognized as one of the leading causes of death in the world. Therefore, the present study aimed to evaluate the performance of classification models in order to predict Myocardial Infarction, using a feature selection method tha...

Most common feature selection techniques for document categorization are supervised and require lots of training data in order to accurately capture the descriptive and discriminative information from the defined categories. Considering that training sets are extremely small in many classification tasks, in this paper we explore the use of unsupervised extractive summarization as a feature sele...

High dimensionality has been a major problem for gene array-based cancer classification. Feature Selection (FS) is ordinarily used as a useful technique in order to reduce the dimension of the dataset. For that to get advantages from both methods of feature selection, Individual Feature Ranking (IFR) and Feature Subset Selection (FSS) are combined which is a hybrid approach. Information Gain fr...