Our evaluation of the constructive algorithms is based on seven criteria: four network structure based and three learning properties based. This evaluation involves analyzing their common properties, common drawbacks and discussing possible future constructive neural network algorithms which will give clear understanding of current literature and possible future research directions. This study aims to evaluate constructive neural network algorithms by clustering them. The constructive algorithms have been generally accepted due to their advantages over the pruning algorithms, regularization and population-based search algorithms.
Majority of the previous research is constructive in nature, aiming the enlargement of a small network by adding new nodes until the desired error level is reached. It has been a challenge to find the optimal network size of ANN model.
Artificial neural networks (ANN) have been a powerful data mining tool with no prior data assumptions and non-linear reasoning ability.
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