Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines

Abstract EN

Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN).

It often has good generalization performance.

However, there are chances that it might overfit the training data due to having more hidden nodes than needed.

To address the generalization performance, we use a heterogeneous ensemble approach.

We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, L2-norm-optimized ELM (ELML2), and Kernel-ELM.

The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling.

The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble.

Finally, the class label of unseen data is predicted using majority vote approach.

Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble).

The validity of AELME is confirmed through classification on several real-world benchmark datasets.