Autoassociator-based models for speaker verification
Pattern Recognition Letters
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning When Negative Examples Abound
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
Support Vector Data Description
Machine Learning
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Extreme re-balancing for SVMs: a case study
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Response modeling with support vector machines
Expert Systems with Applications: An International Journal
Constructing response model using ensemble based on feature subset selection
Expert Systems with Applications: An International Journal
SOM-based novelty detection using novel data
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
A neural network-based model for paper currency recognition and verification
IEEE Transactions on Neural Networks
Evaluation of k-Nearest Neighbor classifier performance for direct marketing
Expert Systems with Applications: An International Journal
Accounting for the long-term effects of a marketing contact
Expert Systems with Applications: An International Journal
Learning without default: a study of one-class classification and the low-default portfolio problem
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing
Expert Systems with Applications: An International Journal
Improved response modeling based on clustering, under-sampling, and ensemble
Expert Systems with Applications: An International Journal
Pattern selection for support vector regression based response modeling
Expert Systems with Applications: An International Journal
Save the best for last? The treatment of dominant predictors in financial forecasting
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
This paper proposes to use novelty detection approaches to alleviate the class imbalance in response modeling. Two novelty detectors, one-class support vector machine (1-SVM) and learning vector quantization for novelty detection (LVQ-ND), are compared with binary classifiers for a catalogue mailing task with DMEF4 dataset. The novelty detectors are more accurate and more profitable when the response rate is low. When the response rate is relatively high, however, a support vector machine model with modified misclassification costs performs the best. In addition, the novelty detectors turn in higher profits with a low mailing cost, while the SVM model is the most profitable with a high mailing cost.