Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
C4.5: Programs for Machine Learning
C4.5: Programs for Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Environments conducive to evolution of modularity
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
IEEE Transactions on Neural Networks
Efficient classification for multiclass problems using modular neural networks
IEEE Transactions on Neural Networks
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In this paper, we propose a sequential decomposition method for multi-class pattern classification problems based on domain knowledge. A novel modular decision tree architecture is used to divide a K-class classification problem into a series of L smaller (binary or multiclass) sub-problems. The set of all K classes c = {c1, c2, . . . cK} is divided into smaller subsets (c = {s1, s2, . . . sL}) each of which contains classes that are related to each other. A modular approach is then used to solve (1) the binary sub-problems (pi = {si, si}) and (2) the smaller multiclass problem si = {ci1, ci2, . . . cin}. Problem decomposition helps in a better understanding of the problem without compromising on the classification accuracy. This is demonstrated using the rules generated by the C4.5 classifier using a monolithic system and the modular system.