Feature Selection for Support Vector Machines
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
A selective sampling approach to active feature selection
Artificial Intelligence
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Fault diagnosis system based on rough set theory and support vector machine
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Applying electromagnetism-like mechanism for feature selection
Information Sciences: an International Journal
Hi-index | 0.00 |
A new classification algorithm based on support vector machine and Rough set theory is proposed in the paper. We make great use of the advantages of Rough set theory in dealing with vagueness and uncertainty information, firstly select important features by attribute reduction; secondly select effective samples by rule induction; finally construct support vector classifier by the selected important features and effective samples. Thus it can reduce training samples' dimensions, decrease training samples' scales and noise disturbing. It can provide us with the benefits of improving support vector machine's training speed and classification accuracy. Result of image recognition verifies its efficiency and feasibility. It also provides us an effective method to deal with the large scale and high dimensions data set.