This paper proposes a reduct construction method based on discernibility matrix simplification. The method works with genetic algorithm. To identify potential problems and prevent complete failure of bearings, a new method based on rule-based classifier ensemble is presented. Genetic algorithm is used for feature reduction. The generated rules of the reducts are used to build the candidate base...

Chemical Named Entity Recognition (NER) is the basic step for consequent information extraction tasks such as named entity resolution, drug-drug interaction discovery, extraction of the names of the molecules and their properties. Improvement in the performance of such systems may affects the quality of the subsequent tasks. Chemical text from which data for named entity recognition is extracte...

this paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. in this method, mammograms are segmented into regions of interest (roi) in order to extract features including geometrical and contourlet coefficients. the extracted features benefit from...

In this paper, we present a fully automatic approach to multiple human detection and tracking in high density crowds in the presence of extreme occlusion. Human detection and tracking in high density crowds is an unsolved problem. Standard preprocessing techniques such as background modeling fail when most of the scene is in motion. We integrate human detection and tracking into a single framew...

It is well known that ensembles of predictors produce better accuracy than a single predictor provided there is diversity in the ensemble. This diversity manifests itself as disagreement or ambiguity among the ensemble members. In this paper we focus on ensembles of classifiers based on different feature subsets and we present a process for producing such ensembles that emphasizes diversity (am...

We describe and analyze a new boosting algorithm for deep learning called SelfieBoost. Unlike other boosting algorithms, like AdaBoost, which construct ensembles of classifiers, SelfieBoost boosts the accuracy of a single network. We prove a log(1/ ) convergence rate for SelfieBoost under some “SGD success” assumption which seems to hold in practice.

An ensemble of classifiers is a system consisting of multiple member classifiers which are trained individually and whose outcomes are aggregated into an overall outcome for a testing data instance. Voting is a common approach used to aggregate outcomes generated by member classifiers. Ensembles based on weighted voting have been studied for some time. However, the focus of most studies is more...

An ensemble of classifiers is a set of classifiers whose predictions are combined in some way to classify new instances. Early research has shown that, in general, an ensemble of classifiers is more accurate than any of the single classifiers in the ensemble. Usually the gains obtained by combining different classifiers are more affected by the chosen classifiers than by the used combination. I...