Improvement of Decision Accuracy Using Discretization of Continuous Attributes
نویسندگان
چکیده
The naïve Bayes classifier has been widely applied to decisionmaking or classification. Because the naïve Bayes classifier prefers to dealing with discrete values, an novel discretization approach is proposed to improve naïve Bayes classifier and enhance decision accuracy in this paper. Based on the statistical information of the naïve Bayes classifier, a distributional index is defined in the new discretization approach. The distributional index can be applied to find a good solution for discretization of continuous attributes so that the naïve Bayes classifier can reach high decision accuracy for instance information systems with continuous attributes. The experimental results on benchmark data sets show that the naïve Bayes classifier with the new discretizer can reach higher accuracy than the C5.0 tree.
منابع مشابه
Dynamic Discretization of Continuous Attributes
Discretization of continuous attributes is an important task for certain types of machine learning algorithms. Bayesian approaches, for instance, require assumptions about data distributions. Decision Trees, on the other hand, require sorting operations to deal with continuous attributes , which largely increase learning times. This paper presents a new method of discretization, whose main char...
متن کاملDiscretization of Continuous-valued Attributes and Instance-based Learning
Recent work on discretization of continuous-valued attributes in learning decision trees has produced some positive results. This paper adopts the idea of discretization of continuous-valued attributes and applies it to instance-based learning (Aha, 1990; Aha, Kibler & Albert, 1991). Our experiments have shown that instance-based learning (IBL) usually performs well in continuous-valued attribu...
متن کاملDiscretization oriented to Decision Rules Generation
Many of the supervised learning algorithms only work with spaces of discrete attributes. Some of the methods proposed in the bibliography focus on the discretization towards the generation of decision rules. This work provides a new discretization algorithm called USD (Unparametrized Supervised Discretization), which transforms the infinite space of the values of the continuous attributes in a ...
متن کاملA novel approach for discretization of continuous attributes in rough set theory
Discretization of continuous attributes is an important task in rough sets and many discretization algorithms have been proposed. However, most of the current discretization algorithms are univariate, which may reduce the classification ability of a given decision table. To solve this problem, we propose a supervised and multivariate discretization algorithm — SMDNS in rough sets, which is deri...
متن کاملAn Evolutionary Algorithm Using Multivariate Discretization for Decision Rule Induction
We describe EDRL-MD, an evolutionary algorithm-based system, for learning decision rules from databases. The main novelty of our approach lies in dealing with continuous valued attributes. Most of decision rule learners use univariate discretization methods, which search for threshold values for one attribute at the same time. In contrast to them, EDRL-MD simultaneously searches for threshold v...
متن کامل