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Performance Improvement of a Parsimonious Learning Machine Using Metaheuristic Approaches
- Source :
- IEEE Transactions on Cybernetics. 52:7277-7290
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- Autonomous learning algorithms operate in an online fashion in dealing with data stream mining, where minimum computational complexity is a desirable feature. For such applications, parsimonious learning machines (PALMs) are suitable candidates due to their structural simplicity. However, these parsimonious algorithms depend upon predefined thresholds to adjust their structures in terms of adding or deleting rules. Besides, another adjustable parameter of PALM is the fuzziness in membership grades. The best set of such hyper parameters is determined by experts' knowledge or by optimization techniques such as greedy algorithms. To mitigate such experts' dependency or usage of computationally expensive greedy algorithms, in this work, a meta heuristic-based optimization technique, called the multimethod-based optimization technique (MOT), is utilized to develop an advanced PALM. The performance has been compared with some popular optimization techniques, namely, the greedy search, local search, genetic algorithm (GA), and particle swarm optimization (PSO). The proposed parsimonious learning algorithm with MOT outperforms the others in most cases. It validates the multioperator-based optimization technique's advantages over the single operator-based variants in selecting the best feasible hyperparameters for the autonomous learning algorithm by maintaining a compact architecture.
- Subjects :
- Hyperparameter
Computational complexity theory
Data stream mining
business.industry
Computer science
Particle swarm optimization
Machine learning
computer.software_genre
Computer Science Applications
Human-Computer Interaction
Control and Systems Engineering
Genetic algorithm
Feature (machine learning)
Data Mining
Local search (optimization)
Artificial intelligence
Electrical and Electronic Engineering
Greedy algorithm
business
computer
Metaheuristic
Algorithms
Software
Information Systems
Subjects
Details
- ISSN :
- 21682275 and 21682267
- Volume :
- 52
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Cybernetics
- Accession number :
- edsair.doi.dedup.....8592bd3438c988bd9412c7fd57dc0015