Disparity in Intelligent Classification of Data Sets Due to Dominant Pattern Effect (DPE)

Abstract

A hypothesis of the existence of dominant pattern that may affect the performance of a neural based pattern recognition system and its operation in terms of correct and accurate classification, pruning and optimization is assumed, presented, tested and proved to be correct. Two sets of data subjected to the same ranking process using four main features are used to train a neural network engine separately and jointly. Data transformation and statistical pre-processing are carried out on the datasets before inserting them into the specifically designed multi-layer neural network employing Weight Elimination Algorithm with Back Propagation (WEA-BP). The dynamics of classification and weight elimination process is correlated and used to prove the dominance of one dataset. The presented results proved that one dataset acted aggressively towards the system and displaced the first dataset making its classification almost impossible. Such modulation to the relationships among the selected features of the affected dataset resulted in a mutated pattern and subsequent re-arrangement in the data set ranking of its members.