Suppression based immune mechanism to find arepresentative training set in data classification tasks
Abstract
Reference
Index Terms
- Suppression based immune mechanism to find arepresentative training set in data classification tasks
Recommendations
Immune network based ensembles
This paper presents a new method for constructing ensembles of classifiers based on immune network theory, one of the most interesting paradigms within the field of artificial immune systems. Ensembles of classifiers are a very interesting alternative ...
Text classification method based on self-training and LDA topic models
A novel text classification method for learning from very small labeled set.The method uses a text representation based on the LDA topic model.Self-training is used to enlarge labeled set from unlabeled instances.A model for setting methods parameters ...
A simple artificial immune system (SAIS) for generating classifier systems
AI'06: Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial IntelligenceCurrent artificial immune system (AIS) classifiers have two major problems: (1) their populations of B-cells can grow to huge proportions and (2) optimizing one B-cell (part of the classifier) at a time does not necessarily guarantee that the B-cell ...
Comments
Information & Contributors
Information
Published In

Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Article
Conference
Acceptance Rates
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 138Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in