On balancing between optimal and proportional categorical predictions

Abstract

A bias-variance dilemma in categorical data mining and analysis is the fact that a prediction method can aim at either maximizing the overall point-hit accuracy without constraint or with the constraint of minimizing the distribution bias. However, one can hardly achieve both at the same time. A scheme to balance these two prediction objectives is proposed in this article. An experiment with a real data set is conducted to demonstrate some of the scheme's characteristics. Some basic properties of the scheme are also discussed.

Increase statistical reliability without losing predictive power by merging classes and adding variables

“…The prediction accuracy naturally attracts most of the attention and has been studied for hundreds of years. Categorical data analysis alone has the rate of pointhit accuracy, of distribution bias and of the balanced one between them [9]. Huang, Shi and Wang [12] suggested that the measure of association is fundamental to obtain the prediction accuracy rate and that this measure will increase as more explanatory variables added in that probabilistic model [12].…”

Forward supervised discretization for multivariate with categorical responses

“…The choosing of a variance measure depends on the objective of the data analysis, the predictive model to be used, or even on the analyst's preference: the Gini, entropy and Chisquare are typical preferences. In this article, we choose the proportional and the modal prediction oriented variances, the GK-tau and GK-lambda, as in ( [11,12,13,8,18], for their statistical interpretability.…”