A new pruning method for extreme learning machines via genetic algorithms

作者：Alencar Alisson S C; Rocha Neto Ajalmar R; Gomes Joao Paulo P

来源：Applied Soft Computing, 2016, 44: 101-107.

DOI：10.1016/j.asoc.2016.03.019

• Extreme learning machines
• Pruning methods
• Genetic algorithms

Extreme learning machine (ELM) is a recently proposed learning algorithm for single hidden layer feed-foward neural networks (SLFN) that achieved remarkable performances in various applications. In ELM, the hidden neurons are randomly assigned and the output layer weights are learned in a single step using the Moore-Penrose generalized inverse. This approach results in a fast learning neural network algorithm with a single hyperparameter (the number of hidden neurons). Despite the aforementioned advantages, using ELM can result in models with a large number of hidden neurons and this can lead to poor generalization. To overcome this drawback, we propose a novel method to prune hidden layer neurons based on genetic algorithms (GA). The proposed approach, referred as GAP-ELM, selects subset of the hidden neurons to optimize a multiobjective fitness function that defines a compromise between accuracy and the number of pruned neurons. The performance of GAP-ELM is assessed on several real world datasets and compared to other SLFN and a well known pruning method called Optimally Pruned ELM (OP-ELM). On the basis of our experiments, we can state that GAP-ELM is a valid alternative for classification tasks.

• 出版日期2016-7