On the Error-Reject Trade-Off in Biometric Verification Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
Using Representative-Based Clustering for Nearest Neighbor Dataset Editing
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
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
An adaptive hybrid and cluster-based model for speeding up the k-NN classifier
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
A simple noise-tolerant abstraction algorithm for fast k-NN classification
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
| Hi-index | 0.00 |
Response Modeling is concerned with computing the likelihood of a customer to respond to a marketing campaign. A major problem encountered in response modeling is huge volume of data or patterns. The k-NN has been used in various classification problems for its simplicity and ease of implementation. However, it has not been applied to problems for which fast classification is needed since the classification time rapidly increases as the size of reference set increases. In this paper, we propose a clustering-based preprocessing step in order to reduce the size of reference set. The experimental results showed an 85% decrease in classification time without a loss of accuracy.