TITLE: Investigating Data Preprocessing Methods For Circuit Complexity Models

AUTHORS: P.W.C. Prasad and Azam Beg

PUBLICATION/VENUE: Expert Systems with Applications, vol. 36, no. 1, Jan 2009, pp. 519-526. [Impact factor = 2.596].


Preprocessing the data is an important step while creating neural network (NN) applications because this step usually has a significant effect on the prediction performance of the model. This paper compares different data processing strategies for NNs for prediction of Boolean function complexity (BFC). We compare NNsí predictive capabilities with (1) no preprocessing (2) scaling the values in different curves based on every curveís own peak and then normalizing to [0, 1] range (3) applying z-score to values in all curves and then normalizing to [0, 1] range, and (4) logarithmically scaling all curves and then normalizing to [0, 1] range. The efficiency of these methods was measured by comparing RMS errors in NN-made BFC predictions for numerous ISCAS benchmark circuits. Logarithmic preprocessing method resulted in the best prediction statistics as compared to other techniques.


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