IDD: A Supervised Interval Distance-Based Method for Discretization

A Supervised Discretization Method for Quantitative and Qualitative Ordered Variables

In this work, a new technique to define cut-points in the discretization process of a continuous attribute is presented. This method is used as a prior step in a regression problem, considered as a learning problem in which the output variable can be either quantitative (continuous or discreet) or qualitative defined over an ordinal scale. The proposed method emphasizes the concept of location ...

A Supervised Method for Constructing Sentiment Lexicon in Persian Language

Due to the increasing growth of digital content on the internet and social media, sentiment analysis problem is one of the emerging fields. This problem deals with information extraction and knowledge discovery from textual data using natural language processing has attracted the attention of many researchers. Construction of sentiment lexicon as a valuable language resource is a one of the imp...

A Method for Defuzzication by Weighted Distance

A Bayesian approach for supervised discretization

In supervised machine learning, some algorithms are restricted to discrete data and thus need to discretize continuous attributes. In this paper, we present a new discretization method called MODL, based on a Bayesian approach. The MODL method relies on a model space of discretizations and on a prior distribution defined on this model space. This allows the setting up of an evaluation criterion...

A greedy algorithm for supervised discretization

We present a greedy algorithm for supervised discretization using a metric defined on the space of partitions of a set of objects. This proposed technique is useful for preparing the data for classifiers that require nominal attributes. Experimental work on decision trees and naïve Bayes classifiers confirm the efficacy of the proposed algorithm.