A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Applying an existing machine learning algorithm to text categorization
Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing
Learning Classifier Systems, From Foundations to Applications
Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Learning concept classification rules using genetic algorithms
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
| Hi-index | 0.00 |
More and more people rely on e-mails rather than postal letters to communicate to each other. Although e-mails are more convenient, letters still have many positive features. The ability to handle "anonymous recipient" is one of them. This paper proposes a software agent that performs the routing task as human beings for the anonymous recipient e-mails. The software agent named "TWIMC (To Whom It May Concern)" receives anonymous recipient e-mails, analyze it, and then routes the e-mail to the mostly qualified person (i.e., email account) inside the organization. The agent employs the Set-oriented Classifier System (SCS) that is a genetic algorithm classifier that uses set representation internally. The comparison of SCS with the Support Vector Machine (SVM) shows that the SCS outperforms SVM under noisy environment.