Constructing response model using ensemble based on feature subset selection

Authors:
Enzhe Yu;Sungzoon Cho
Affiliations:
Department of Industrial Engineering, Seoul National University, San 56-1 Shilimdong Kwanakgu, 151-744 Seoul, South Korea;Department of Industrial Engineering, Seoul National University, San 56-1 Shilimdong Kwanakgu, 151-744 Seoul, South Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2006

Citing 9
Cited 11

The Strength of Weak Learnability

Machine Learning
Neural networks and the bias/variance dilemma

Neural Computation
Bagging predictors

Machine Learning
Optimal linear combinations of neural networks

Neural Networks
The Random Subspace Method for Constructing Decision Forests

IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning When Negative Examples Abound

ECML '97 Proceedings of the 9th European Conference on Machine Learning
Mining customer product ratings for personalized marketing

Decision Support Systems - Special issue: Web data mining
An intelligent system for customer targeting: a data mining approach

Decision Support Systems
Making use of population information in evolutionary artificialneural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Focusing on non-respondents: Response modeling with novelty detectors

Expert Systems with Applications: An International Journal
Response modeling with support vector regression

Expert Systems with Applications: An International Journal
Clustering-Based Reference Set Reduction for k-Nearest Neighbor

ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Using a hybrid meta-evolutionary rule mining approach as a classification response model

Expert Systems with Applications: An International Journal
Diversity of ability and cognitive style for group decision processes

Information Sciences: an International Journal
A hybrid novelty score and its use in keystroke dynamics-based user authentication

Pattern Recognition
Accounting for the long-term effects of a marketing contact

Expert Systems with Applications: An International Journal
Optimal selection of potential customer range through the union sequential pattern by using a response model

Expert Systems with Applications: An International Journal
Global optimization of support vector machines using genetic algorithms for bankruptcy prediction

ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Pattern selection for support vector regression based response modeling

Expert Systems with Applications: An International Journal
Financial distress prediction using support vector machines: Ensemble vs. individual

Applied Soft Computing

Quantified Score

Hi-index	12.06

Visualization

Abstract

In building a response model, determining the inputs to the model has been an important issue because of the complexities of the marketing problem and limitations of mental models for decision-making. It is common that the customers' historical purchase data contains many irrelevant or redundant features thus result in bad model performance. Furthermore, single complex models based on feature subset selection may not always report improved performance largely because of overfitting and instability. Ensemble is a widely adopted mechanism for alleviating such problems. In this paper, we propose an ensemble creation method based on GA based wrapper feature subset selection mechanism. Through experimental studies on DMEF4 data set, we found that the proposed method has, at least, two distinct advantages over other models: first, the ability to account for the important inputs to the response model; second, improved prediction accuracy and stability.