In this paper, a new classification method (ADCC) for high dimensional data is proposed. In this method, a decision cluster classification model (DCC) consists of a set of disjoint decision clusters, each labeled with a dominant class that determines the class of new objects falling in the cluster. A cluster tree is first generated from a training data set by recursively calling a variable weighting k-means algorithm. Then, the DCC model is selected from the tree. Anderson-Darling test is used to determine the stopping condition of the tree growing. A series of experiments on both synthetic and real data sets have shown that the new classification method (ADCC) performed better in accuracy and scalability than the existing methods of k-NN, decision tree and SVM. It is particularly suitable for large, high dimensional data with many classes.
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||21st Australasian Joint Conference on Artificial Intelligence, AI 2008|
|Period||1/12/08 → 5/12/08|
- Theoretical Computer Science
- Computer Science(all)