Hidden Inverted Specific-Class Distance Measure for Nominal Attributes

The inverted specific-class distance measure (ISCDM) is a popular distance metric that uses conditional probability term to calculate the distance between two nominal attribute values, but the reliability of the conditional probability term is limited by the attribute independence assumption, which leads to the suboptimal performance in applications involving sophisticated attribute dependencies. To obtain more accurate conditional probability estimation, in this study, we derive an enhanced ISCDM by leveraging structure extension to alleviate the unrealistic attribute assumption. We denominate the resulting model as the hidden inverted specific-class distance measure (HISCDM). In HISCDM, the structure extension scheme of hidden naive bayes is adopted to find the weighted dependence relationships between attributes, and then is incorporated into the conditional probability estimation. The comprehensive experimental results demonstrate that our proposed HISCDM significantly outperforms all other methods used for comparison in terms of classification accuracy.