How to Deal with Large Dataset, Class Imbalance and Binary Output in SVM based Response Model
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[Abstract]: Various machine learning methods have made a rapid transition to response modeling in search of improved performance. And support vector machine (SVM) has also been attracting much attention lately. This paper presents an SVM response model. We are specifically focusing on the how-tos to circumvent practical obstacles, such as how to face with class imbalance problem, how to produce the scores from an SVM classifier for lift chart analysis, and how to evaluate the models on accuracy and profit. Besides coping with the intractability problem of SVM training caused by large marketing dataset, a previously proposed pattern selection algorithm is introduced. SVM training accompanies time complexity of the cube of training set size. The pattern selection algorithm picks up important training patterns before SVM response modeling. We made comparison on SVM training results between the pattern selection algorithm and random sampling. Three aspects of SVM response models were evaluated: accuracies, lift chart analysis, and computational efficiency. The SVM trained with selected patterns showed a high accuracy, a high uplift in profit and in response rate, and a high computational efficiency.
| Author(s): | Shin, H. and Cho, S. |
| Links: | |
| Journal: | Proc. of the Korean Data Mining Conference |
| Pages: | 93-107 |
| Year: | 2003 |
| Month: | December |
| Day: | 0 |
| Bibtex Type: | Conference Paper (inproceedings) |
| Event Name: | Korean Data Mining Conference |
| Event Place: | Seoul, Korea |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Institution: | Seoul National University, Seoul, Korea |
| Note: | Best Paper Award |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTex
@inproceedings{2709,
title = {How to Deal with Large Dataset, Class Imbalance and Binary Output in SVM based Response
Model},
journal = {Proc. of the Korean Data Mining Conference},
abstract = {[Abstract]: Various machine learning methods have made a rapid transition to response modeling in search of improved
performance. And support vector machine (SVM) has also been attracting much attention lately. This paper presents an
SVM response model. We are specifically focusing on the how-tos to circumvent practical obstacles, such as how to
face with class imbalance problem, how to produce the scores from an SVM classifier for lift chart analysis, and how
to evaluate the models on accuracy and profit. Besides coping with the intractability problem of SVM training caused
by large marketing dataset, a previously proposed pattern selection algorithm is introduced. SVM training accompanies
time complexity of the cube of training set size. The pattern selection algorithm picks up important training patterns
before SVM response modeling. We made comparison on SVM training results between the pattern selection algorithm and random sampling. Three aspects of SVM response models were evaluated: accuracies, lift chart analysis, and computational efficiency. The SVM trained with selected patterns showed a high accuracy, a high uplift in profit and
in response rate, and a high computational efficiency.},
pages = {93-107},
organization = {Max-Planck-Gesellschaft},
institution = {Seoul National University, Seoul, Korea},
school = {Biologische Kybernetik},
month = dec,
year = {2003},
note = {Best Paper Award},
slug = {2709},
author = {Shin, H. and Cho, S.},
month_numeric = {12}
}