Evolutionary parallel extreme learning machines for the data classification problem. (April 2019) - Dokeroglu, Tansel; Sevinc, Ender
Highlights: First evolutionary parallel ELM algorithm for the data classification problem. The ELM is enhanced with the feature selection. The proposed algorithm tunes its parameters at run time. The scalability of the proposed algorithm is (near)-linear. The state-of-the-art algorithms are outperformed. Abstract: This study proposes an Island Parallel Evolutionary Extreme Learning Machine algorithm (IPE-ELM) for the well-known data classification problem. The ELM is a fast and efficient machine learning technique with its single-hidden layer feed-forward neural network (SLFN). High prediction accuracy and learning speed of the ELM make it an elegant tool for the fitness calculation process of the evolutionary algorithms. The IPE-ELM algorithm combines the evolutionary genetic algorithms (for feature selection), ELM machine learning technique (for prediction accuracy calculation), parallel computation (for faster fitness evaluation), and parameter tuning (activation function selection and the number of hidden neurons) for the solution of this important problem. Each ELM that runs at a different processor selects one of four different activation functions ( Sine, Cosine, Sigmoid and Hyperbolic Tangent ) and uses a randomized number of hidden neurons to achieve higher prediction accuracy. The proposed algorithm provides high quality results with its (near)-linear scalability behavior. The IPE-ELM algorithm is compared with state-of-the-art data classification algorithms by using UCI benchmark datasets and significant improvements are reported in terms of prediction accuracy with reasonable execution times. The scalable IPE-ELM algorithm can be reported as the first island parallel evolutionary classification algorithm with its high prediction accuracy results that outperforms state-of-the-art algorithms in literature.