On Balancing between Optimal and Proportional categorical predictions

Wenxue Huang; Yuanyi Pan; Wenxue Huang; Yuanyi Pan

doi:10.3934/bdia.2016.1.129

Big Data and Information Analytics

2016, Volume 1, Issue 1: 129-137. doi: 10.3934/bdia.2016.1.129

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On Balancing between Optimal and Proportional categorical predictions

Wenxue Huang ¹,
Yuanyi Pan ^{2
,
,}

1.
Department of Mathematics, Guangzhou University Guangzhou, Guangdong 510006, China;
2.
Kochava Inc 414 Church Street, Suite 306 Sandpoint, Idaho 83864, USA

Received: 01 May 2015 Published: 01 January 2016

Abstract
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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.
- Bias-variance dilemma,
- categorical data,
- optimal prediction,
- proportional prediction,
- point estimation,
- conditional distribution
Citation: Wenxue Huang, Yuanyi Pan. On Balancing between Optimal and Proportional categorical predictions[J]. Big Data and Information Analytics, 2016, 1(1): 129-137. doi: 10.3934/bdia.2016.1.129

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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.

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