On balancing between optimal and proportional categorical predictions
Wenxue Huang - Department of Mathematics, Guangzhou University, Guangzhou, Guangdong 510006, China (email)
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.
Keywords: Bias-variance dilemma, categorical data, optimal prediction, proportional prediction, point estimation, conditional distribution.
Received: May 2015; Revised: August 2015; Available Online: September 2015.