Abstract | ||
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Ant colony optimization (ACO) has been successfully applied to classification, where the aim is to build a model that captures the relationships between the input attributes and the target class in a given domain’s dataset. The constructed classification model can then be used to predict the unknown class of a new pattern. While artificial neural networks are one of the most widely used models for pattern classification, their application is commonly restricted to fully connected three-layer topologies. In this paper, we present a new algorithm, ANN-Miner, which uses ACO to learn the structure of feed-forward neural networks. We report computational results on 40 benchmark datasets for several variations of the algorithm. Performance is compared to the standard three-layer structure trained with two different weight-learning algorithms (back propagation, and the algorithm), and also to a greedy algorithm for learning NN structures. A nonparametric Friedman test is used to determine statistical significance. In addition, we compare our proposed algorithm with NEAT, a prominent evolutionary algorithm for evolving neural networks, as well as three different well-known state-of-the-art classifiers, namely the C4.5 decision tree induction algorithm, the Ripper classification rule induction algorithm, and support vector machines. |
Year | DOI | Venue |
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2015 | 10.1007/s11721-015-0112-z | Swarm Intelligence |
Keywords | Field | DocType |
Ant colony optimization (ACO),Machine learning,Pattern classification,Neural networks | Ant colony optimization algorithms,Decision tree,Classification rule,Evolutionary algorithm,Computer science,Artificial intelligence,Artificial neural network,Pattern recognition,Support vector machine,Algorithm,Greedy algorithm,Backpropagation,Machine learning | Journal |
Volume | Issue | ISSN |
9 | 4 | 1935-3812 |
Citations | PageRank | References |
13 | 0.58 | 70 |
Authors | ||
2 |
Name | Order | Citations | PageRank |
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Khalid M. Salama | 1 | 160 | 13.09 |
Ashraf M. Abdelbar | 2 | 243 | 25.43 |