Flexible-Hybrid Sequential Floating Search in Statistical Feature Selection

Among recent topics studied in context of feature selection the hybrid algorithms seem to receive particular attention. In this paper we propose a new hybrid algorithm, the flexible hybrid floating sequential search algorithm, that combines both the filter and wrapper search principles. The main benefit of the proposed algorithm is its ability to deal flexibly with the quality-of-result versus computational time tradeoff and to enable wrapper based feature selection in problems of higher dimensionality than before. We show that it is possible to trade significant reduction of search time for negligible decrease of the classification accuracy. Experimental results are reported on two data sets, WAVEFORM data from the UCI repository and SPEECH data from British Telecom.