A combinatorial cooperative-tabu search feature reduction approach

Presenting an efficient general feature selection method for the problem of the curse of dimensionalityis still an open problem in pattern recognition, and, considering the cooperation among featuresthrough search processes, it is the most important challenge. in this paper, a combinatorial approach hasbeen proposed, which consists of three feature reduction algorithms that have been applied in a parallelmanner to cooperate. we consider each of these algorithms as a component in a reduction framework. foreach component, among all various attribute selection algorithms, the tabu search (ts) a useful and stateof the art algorithm, is used. to take account of the interaction between features, more subsets should beexamined. hence, each component should explore individually through feature space in a local area whichis different from other components. the proposed algorithm, called the cooperative-tabu-search (cts),and also a revised version of this new method, is introduced to accelerate the convergence. after sufficientiterations, which satisfy the objective function; the final subset has been selected by voting between threereduction phases, and the data is then transformed into the new space, where the data are classified withsome commonly used classifiers, such as nearest neighbor (nn) and support vector machine (svm). theemployed benchmark of this paper is chosen among the uci datasets to evaluate the proposed methodcompared to others. the experimental results show the supremacy of the accuracy of the implementedcombinatorial approach in comparison with traditional methods.