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Abstract

Decision tree induction, as well as other inductive learning methods, requires training data of high quality to be able to generate accurate and reliable classification models. Example cases should form a representative sample from the application area, and the attributes used to describe example cases should be relevant and adequate for the classification task to be solved. In this paper, measures of the strength of association and an entropy-based approach have been used to assess the quality of the training data. Studied classification tasks related to three otological data sets: a conscript data set, a vertigo data set, and a postoperative nausea and vomiting data set. The paper suggests that the studied approaches give some guidelines about the quality of the training data, but other approaches are also needed to guide training data building.

Decision Tree Analysis of Traditional Risk Factors of Carotid Atherosclerosis and a Cutpoint-Based Prevention Strategy

“…Traditional statistical methods such as logistic regression analysis have been effectively applied and widely recognized in the screening of disease risk factors. However, logistic regression has limitations in managing complicated data because many factors are involved that could affect each other [22] , [23] . Decision tree analysis might prevent such disadvantages.…”

Generalized Traditional Risk Factors of Being Overweight and Obesity among Ever Married Non-Pregnant Women in Bangladesh: An Application of Decision Tree Analysis

“…Furthermore, previous studies demonstrate that in order to determine the risk factors of overweight, logistic regression method was used. However, logistic regression has limitation in managing complicated data because many factors are involved that could affect each other's [18,19]. Such disadvantage could easily be overcome by decision tree analysis [20,21], screen for the most important risk factors and identify the cut points for risk factors.…”