Combining Evolutionary, Connectionist, and Fuzzy Classification Algorithms for Shape Analysis

This paper presents an investigation into the classification of a difficult data set containing large intra-class variability but low inter-class variability. Standard classifiers are weak and fail to achieve satisfactory results however, it is proposed that a combination of such weak classifiers can improve overall performance. The paper also introduces a novel evolutionary approach to fuzzy rule generation for classification problems.

• Type: Conference
• Year: 2000
• Where: EVOW
• Authors: Paul L. Rosin, Henry O. Nyongesa
• Keywords: Artificial Intelligence, EVOW 2000, Large Intra-class Variability, Low Inter-class Variability, Weak Classifiers