6533b838fe1ef96bd12a3df7
RESEARCH PRODUCT
Feature extraction for classification in knowledge discovery systems
M. PechenizkiyS. PuuronenA. TsymbalV. PaladeR.j. HowlettL.c. Jainsubject
Decision support systembusiness.industryComputer scienceDimensionality reductionFeature extractionMachine learningcomputer.software_genreKnowledge acquisitionk-nearest neighbors algorithmKnowledge extractionFeature (computer vision)Artificial intelligenceData miningbusinesscomputerCurse of dimensionalitydescription
Dimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of "the curse of dimensionality". We consider three different eigenvector-based feature extraction approaches for classification. The summary of obtained results concerning the accuracy of classification schemes is presented and the issue of search for the most appropriate feature extraction method for a given data set is considered. A decision support system to aid in the integration of the feature extraction and classification processes is proposed. The goals and requirements set for the decision support system and its basic structure are defined. The means of knowledge acquisition needed to build up the proposed system are considered.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2003-01-01 | Knowledge-Based Intelligent Information and Engineering Systems (Proceedings 7th International Conference, KES 2003, Oxford, UK, September 3-5, 2003), Part I |