Conversion of categorical variables into numerical variables via Bayesian network classifiers for binary classifications

Namgil Lee, Jong Min Kim

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Many pattern classification algorithms such as Support Vector Machines (SVMs), Multi-Layer Perceptrons (MLPs), and K-Nearest Neighbors (KNNs) require data to consist of purely numerical variables. However many real world data consist of both categorical and numerical variables. In this paper we suggest an effective method of converting the mixed data of categorical and numerical variables into data of purely numerical variables for binary classifications. Since the suggested method is based on the theory of learning Bayesian Network Classifiers (BNCs), it is computationally efficient and robust to noises and data losses. Also the suggested method is expected to extract sufficient information for estimating a minimum-error-rate (MER) classifier. Simulations on artificial data sets and real world data sets are conducted to demonstrate the competitiveness of the suggested method when the number of values in each categorical variable is large and BNCs accurately model the data.

    Original languageEnglish (US)
    Pages (from-to)1247-1265
    Number of pages19
    JournalComputational Statistics and Data Analysis
    Volume54
    Issue number5
    DOIs
    StatePublished - May 1 2010

    Fingerprint

    Dive into the research topics of 'Conversion of categorical variables into numerical variables via Bayesian network classifiers for binary classifications'. Together they form a unique fingerprint.

    Cite this