Feature selection (FS) is a well-known preprocess step in soft computing and machine learning algorithms. It plays critical role different real-world applications since it aims to determine the relevant features remove other ones. This process (i.e., FS) reduces time space complexity of technique used handle collected data. The feature methods based on metaheuristic (MH) techniques established ...

In this paper, the problem of classifying a HTML documents into a hierarchy of categories is investigated in the context of cooperative information repository, named WebClassII. The hierarchy of categories is involved in all aspects of automated document classification, namely feature extraction, learning, and classification of a new document. Innovative aspects of this work are: a) an experime...

(i) RankSVM SVM based pairwise ranker. (ii) RankBoost Weak ranker based pairwise ranker that uses boosting. (iii) LambdaMART LambdaMART uses gradient boosting to optimize a ranking cost function. Baseline 1: FS-BFS The FS-BFS is a wrapper based approach of feature selection for ranking [Dang and Croft, 2010]. The method partitions the F into non-overlapping k subsets and learns a ranking model ...

Two histopathologically different kinds of rhabdomyosarcoma (RMS) -alveolar and embryonal RMSare associated with distinct clinical characteristics and different cytogenetic properties. Affymetrix microarrays (U133A/B) were used to characterize the 74 tumoral tissues of both kinds. For consistency with previous work, 8801 genes have been considered in our analysis. Also, the train/test division ...

The Clustering is a method of grouping the information into modules or clusters. Their dimensionality increases usually with a tiny number of dimensions that are significant to definite clusters, but data in the unrelated dimensions may produce much noise and wrap the actual clusters to be exposed. Attribute subset selection method is frequently used for data reduction through removing unrelate...

We propose an optimized visual tracking algorithm based on the real-time selection of locally and temporally discriminative features. A novel feature selection mechanism is embedded in the Adaptive Color Names [2] (ACT) tracking system that adaptively selects the top-ranked discriminative features for tracking. The Dynamic Feature Selection Tracker (DFST) provides a significant gain in accuracy...

Feature Selection (FS) has become the focus of much research on decision support systems areas for which datasets with tremendous number of variables are analyzed. In this paper we present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic Algorithm (GA) wrapped Bayes Naïve (BN) based FS. Basically, CAD dataset contains two classes defined with 13 features. In G...

When constructing a Bayesian network classifier from data, the more or less redundant features included in a dataset may bias the classifier and as a consequence may result in a relatively poor classification accuracy. In this paper, we study the problem of selecting appropriate subsets of features for such classifiers. To this end, we propose a new definition of the concept of redundancy in no...