Hybrid machine learning to improve predictive performance

Authors:
Sung Ho Ha;Jong Sik Jin;Seong Hyeon Joo
Affiliations:
School of Business Administration, Kyungpook National University, Daegu, Korea;School of Business Administration, Kyungpook National University, Daegu, Korea;School of Business Administration, Kyungpook National University, Daegu, Korea
Venue:
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Year:
2008

Citing 6
Cited 1

A Review and Empirical Evaluation of Feature Weighting Methods for aClass of Lazy Learning Algorithms

Artificial Intelligence Review - Special issue on lazy learning
Semiconductor fabrication facility design using a hybrid search methodology

Computers and Industrial Engineering
Feature Selection for Knowledge Discovery and Data Mining

Feature Selection for Knowledge Discovery and Data Mining
Data mining for yield enhancement in semiconductor manufacturing and an empirical study

Expert Systems with Applications: An International Journal
Recognition of semiconductor defect patterns using spatial filtering and spectral clustering

Expert Systems with Applications: An International Journal
Defect spatial pattern recognition using a hybrid SOM-SVM approach in semiconductor manufacturing

Expert Systems with Applications: An International Journal

Implementation of real-time video conference system using high speed multimedia communication

WSEAS Transactions on Computers

Quantified Score

Hi-index	0.00

Visualization

Abstract

Yield management in semiconductor manufacturing companies requires accurate yield prediction and continual control. This paper presents a hybrid method of combining machine learning techniques to detect high and low yields. In the real applications, the hybrid method provides more accurate yield prediction than other methods.