Bucket Learning: Improving model quality through enhancing local patterns

Authors:
Guangzhi Qu;Hui Wu
Affiliations:
Computer Science and Engineering Department, Oakland University, Rochester, MI 48309, United States;Computer Science and Engineering Department, Oakland University, Rochester, MI 48309, United States
Venue:
Knowledge-Based Systems
Year:
2012

Citing 10
Cited 0

The weighted majority algorithm

Information and Computation
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals

IEEE Transactions on Pattern Analysis and Machine Intelligence
Bagging predictors

Machine Learning
Induction of Decision Trees

Machine Learning
Ensemble Methods in Machine Learning

MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
A Comparison of Decision Tree Ensemble Creation Techniques

IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering Knowledge from Local Patterns with Global Constraints

ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
Improved use of continuous attributes in C4.5

Journal of Artificial Intelligence Research
Clusterer ensemble

Knowledge-Based Systems
A combinational incremental ensemble of classifiers as a technique for predicting students' performance in distance education

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

It is always desirable to improve the quality of a global classification model in the light of the existing models. In this work, the Bucket Learning methodology is first proposed to improve the model quality by enhancing its local patterns. We formally define the concept of a board as a tri-tuple , which unifies the data view, model view and evaluation view of a data mining task. The Bucket Learning framework includes the modules of Boards Generation, Short Boards Discovery, and Short Boards Replacement. A prototypical system is developed to verify the proposed methodology. The experimental results on eight representative data sets from the UCI data repository show that Bucket Learning performs better than traditional classification methods such as J48, AdaBoost, Bagging and LogitBoost. We also demonstrate that the Bucket Learning framework can combine all kinds of data classification models and that the combined model outperforms each individual one.