Ensemble of Efficient Minimal Learning Machines for Classification and Regression
| Autor: | João P. P. Gomes, Diego P. P. Mesquita, Amauri H. Souza Júnior |
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| Rok vydání: | 2017 |
| Předmět: |
Learning classifier system
Computer Networks and Communications business.industry Computer science General Neuroscience Bootstrap aggregating Online machine learning 02 engineering and technology Semi-supervised learning Machine learning computer.software_genre Ensemble learning ComputingMethodologies_PATTERNRECOGNITION Computational learning theory Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering Unsupervised learning 020201 artificial intelligence & image processing Artificial intelligence Instance-based learning Data mining business computer Software |
| Zdroj: | Neural Processing Letters. 46:751-766 |
| ISSN: | 1573-773X 1370-4621 |
| DOI: | 10.1007/s11063-017-9587-5 |
| Popis: | Minimal Learning Machine (MLM) is a recently proposed supervised learning algorithm with performance comparable to most state-of-the-art machine learning methods. In this work, we propose ensemble methods for classification and regression using MLMs. The goal of ensemble strategies is to produce more robust and accurate models when compared to a single classifier or regression model. Despite its successful application, MLM employs a computationally intensive optimization problem as part of its test procedure (out-of-sample data estimation). This becomes even more noticeable in the context of ensemble learning, where multiple models are used. Aiming to provide fast alternatives to the standard MLM, we also propose the Nearest Neighbor Minimal Learning Machine and the Cubic Equation Minimal Learning Machine to cope with classification and single-output regression problems, respectively. The experimental assessment conducted on real-world datasets reports that ensemble of fast MLMs perform comparably or superiorly to reference machine learning algorithms. |
| Databáze: | OpenAIRE |
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