Abstract
Multi-layer neural networks have been successfully applied in a wide range of supervised and unsupervised learning applications. However defining its architecture is a difficult task, and might make their usage very complicated. To solve this problem, a rule-based model, KBANN, was previously introduced making use of an apriori knowledge to build the network architecture. Neithertheless this apriori knowledge is not always available when dealing with real world applications. Other methods presented in the literature propose to find directly the neural network architecture by incrementally adding new hidden neurons (or layers) to the existing network, network which initially has no hidden layer. Recently, a novel neural network approach CLANN based on concept lattices was proposed with the advantage to be suitable for finding the architecture of the neural network when the apriori knowledge is not available. However CLANN is limited to application with only two-class data, which is not often the case in practice. In this paper we propose a novel approach M-CLANN in order to treat multi-class data. Carried out experiments showed the soundness and efficiency of our approach on different UCI datasets compared to standard machine learning systems. It also comes out that M-CLANN model considerably improved CLANN model when dealing with two-class data.
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Mephu Nguifo, E., Tsopzé, N., Tindo, G. (2008). M-CLANN: Multi-class Concept Lattice-Based Artificial Neural Network for Supervised Classification. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_84
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DOI: https://doi.org/10.1007/978-3-540-87559-8_84
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