Institute of Information Theories and Applications FOI ITHEA
Abstract:
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”. Three different eigenvector-based feature extraction approaches are discussed
and three different kinds of applications with respect to classification tasks are considered. The summary of
obtained results concerning the accuracy of classification schemes is presented with the conclusion about the
search for the most appropriate feature extraction method. The problem how to discover knowledge needed to
integrate the feature extraction and classification processes is stated. 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.