Bayes classification approaches with continuous attributes
Shi Chunyiogy · Journal of Tsinghua University(Science and Technology) · 2003
Bayes classification approaches that handle continuous attributes by discretization suffer from the problems of the hardness of determining the number of the discrete intervals, the inability to utilize some prior information and the reduced classification accuracy. As for the above problems, this paper applyied the probabilistic density estimation techniques to Bayes classification with continuous attributes. And studied how to utilize directly the parametric, nonparametric and semiparametric methods for constructing Bayes classifiers containing continuous attributes. Finally, the advantages and disadvantages of the three constructing methods were analyzed, which established the theoretical basis for Bayes classifiers and Bayesian Network classifiers with continuous attributes. The computational example shows the feasibility and effectiveness of the approaches.