Dimensionality reduction of high dimensional data still perceives challenges and hence, it is pertinent to introduce new methods or revamp existing methods. In this study, a new ternary particle swarm optimization (TPSO) algorithm has been proposed, in which particle is a string of “trit”, which is the smallest unit of information. Ternary string is made up of (0, 1 and #). 0, 1 and # are representatives of rejection, acceptance and intermediate (uncertain) states respectively. Since trit is the smallest unit of information therefore, # trit brings the characteristics of quantum theory in the search. This provides the better exploration of feature leading to global optimum solution. This method belongs to wrapper category of feature selection method since it has k nearest neighbor classifier as performance evaluator. The TPSO has been applied in two phases. The second phase is included in the system in a top down information processing fashion, in which big system of information is broken down to have insight into the hidden important information. In first phase TPSO is applied multiple times on each data set. In second phase optimum features are retrieved by applying LBUB, EXP_SEARCH and VOTE_MERGE methods. Experimental results on bench marking datasets show that the proposed methods are promising to handle feature selection in high dimensional space.
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