The twin support vector machine and its extensions have made great achievements in dealing with binary classification problems. However, it suffers from difficulties effective solution of multi-classification fast model selection. This work devotes to the regularization parameter tuning algorithm for multi-class machine. Specifically, a novel sample data set partition strategy is first adopted,...

A support vector machine is a new learning machine; it is based on the statistics learning theory and attracts the attention of all researchers. Recently, the support vector machines SVMs have been applied to the problem of financial early-warning prediction Rose, 1999 . The SVMs-based method has been compared with other statistical methods and has shown good results. But the parameters of the ...

Fault diagnosis has always been an essential aspect of control system design. This is necessary due to the growing demand for increased performance and safety of industrial systems is discussed. Support vector machine classifier is a new technique based on statistical learning theory and is designed to reduce structural bias. Support vector machine classification in many applications in v...

Gene expression data play an important role in the development of efficient cancer diagnoses and classification. The genes identified are subsequently used to classify independent test set samples. The different feature selection methods are investigated and most frequent features are selected among all methods. This paper provides gene selection strategies for multiclass classification that ca...

With the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. One of the major problems in text classification relates to the high dimensional feature spaces. Therefore, the main goal of text classification is to reduce the dimensionality of features space. There are many feature selection methods. However...

the lithologies of regions, which located near the collision zone, are very different from other geology setting. mapping in these areas needs extensive and exact studies and tools because of the variety of rocks, intensive tectonic uplift and complicated units. hyperspectral sensor is one of the most advanced tools with hundreds of bands that each measures a very narrow range of wavelengths an...

background: we aimed to assess the high-risk group for suicide using different classification methods includinglogistic regression (lr), decision tree (dt), artificial neural network (ann), and support vector machine (svm). methods: we used the dataset of a study conducted to predict risk factors of completed suicide in hamadan province, the west of iran, in 2010. to evaluate the high-risk grou...

Background: Electrocardiogram (ECG) is defined as an electrical signal, which represents cardiac activity. Heart rate variability (HRV) as the variation of interval between two consecutive heartbeats represents the balance between the sympathetic and parasympathetic branches of the autonomic nervous system.Objective: In this study, we aimed to evaluate the efficiency of discrete wavelet transfo...

Different approaches have been proposed for feature selection to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods.  In filter methods, features subsets are selected due to some measu...