Investigating data preprocessing methods for circuit complexity models

P. W. Chandana Prasad, Azam Beg

    Research output: Contribution to journalArticlepeer-review

    11 Citations (Scopus)


    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 NNs' predictive capabilities with (1) no preprocessing (2) scaling the values in different curves based on every curve's 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.

    Original languageEnglish
    Pages (from-to)519-526
    Number of pages8
    JournalExpert Systems with Applications
    Issue number1
    Publication statusPublished - Jan 2009


    • Boolean function complexity
    • Computer-aided design
    • Data preprocessing
    • Feed-forward neural network
    • Machine learning
    • Pattern recognition

    ASJC Scopus subject areas

    • General Engineering
    • Computer Science Applications
    • Artificial Intelligence


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