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

Adnan O M Abuassba,Dezheng Zhang,Hazrat Ali,Xiong Luo,Ahmad Shaheryar

doi:10.1155/2017/3405463

Abstract

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.

Highlights

An ensemble learning is a machine learning process to get better prediction performance by strategically combining the predictions from multiple learning algorithms [1]
We will briefly introduce the strengths of the selected base Extreme Learning Machine (ELM) classifiers
In comparison with standard Support Vector Machines (SVM), the Kernel-ELM is less sensitive to the userspecified parameters and has fewer optimization constraints

Summary

Introduction

An ensemble learning is a machine learning process to get better prediction performance by strategically combining the predictions from multiple learning algorithms [1]. In the process of improving ensemble accuracy and stability, different techniques have been established These techniques vary in their approach to treat the training data, the type of algorithms used, and the combination methods followed. ELML2 [10] is a regularized algorithm-based ELM, which has all the basic ELM advantages of regression, binary, and multiclass classification. It introduced a Lagrange multiplier based constraint optimization method. RELM [11] is a constrained and optimized algorithm-based ELM for regression and multiclass classification. RELM makes a tradeoff between the structural (weight norm) and empirical risk (least square error) by regulating a proportion of them during optimization. The reader can refer to [10, 11, 19]

Results

Discussion

Conclusion