Text classification is one of the areas where machine learning algorithms are used. The size dataset and methods used for converting textual words into vectors play a major role in classifying them. This paper proposes heuristic based approach to classify documents using Genetic Algorithm aided Support Vector Machines (SVM) Ensemble Learning approach. real valued representation data done on app...

A model selection method based on tabu search is proposed to build support vector machines (binary decision functions) of reduced complexity and efficient generalization. The aim is to build a fast and efficient support vector machines classifier. A criterion is defined to evaluate the decision function quality which blends recognition rate and the complexity of a binary decision functions toge...

We show that a classifier based on Gaussian mixture models (GMM) can be trained discriminatively to improve accuracy. We describe a training procedure based on the extended Baum-Welch algorithm used in speech recognition. We also compare the accuracy and degree of sparsity of the new discriminative GMM classifier with those of generative GMM classifiers, and of kernel classifiers, such as suppo...

Abstract Due to the worldwide deficiency of medical test kits and significant time required by radiology experts identify new COVID-19, it is essential develop fast, robust, intelligent chest X-ray (CXR) image classification system. The proposed method consists two major components: feature extraction classification. Bag features algorithm creates visual vocabulary from training data categories...

recently, tuning the weights of the rules in fuzzy rule-base classification systems is researched in order to improve the accuracy of classification. in this paper, a margin-based optimization model, inspired by support vector machine classifiers, is proposed to compute these fuzzy rule weights. this approach not only  considers both accuracy and generalization criteria in a single objective fu...

The increasing interest in Support Vector Machines (SVMs) over the past 15 years is described. Methods are illustrated using simulated case studies, and 4 experimental case studies, namely mass spectrometry for studying pollution, near infrared analysis of food, thermal analysis of polymers and UV/visible spectroscopy of polyaromatic hydrocarbons. The basis of SVMs as two-class classifiers is s...

It was introduced in this paper the Optimum-Path Forest for land use classification aiming a better environmental management, using images obtained from CBERS 2B CCD satellite covering the area of the Rio das Pedras watershed, Itatinga City, São Paulo State, Brazil. We also compared the Optimum-Path Forest algorithm with the well known supervised classifiers: Artificial Neural Networks using Mu...

In this paper we study the stability of support vector machines in face detection by decomposing their average prediction error into the bias, variance, and aggregation effect terms. Such an analysis indicates whether bagging, a method for generating multiple versions of a classifier from bootstrap samples of a training set, and combining their outcomes by majority voting, is expected to improv...

This paper presents a multiclass, multilabel implementation of Least Squares Support Vector Machines (LS-SVM) for direction of arrival (DOA) estimation in a CDMA system. For any estimation or classification system the algorithm’s capabilities and performance must be evaluated. Specifically, for classification algorithms a high confidence level must exist along with a technique to automatically ...