Abstract:
Online autonomous learning algorithms mine data streams with minimal computational complexity. Due to their structural simplicity, parsimonious learning machines (PALMs) are suitable for such applications. These parsimonious algorithms use thresholds to add or remove rules. PALM also adjusts membership grade fuzziness. Experts or greedy algorithms determine the best hyperparameters. The multimethod-based optimization technique (MOT) is used to develop an advanced PALM to reduce experts’ dependence on computationally expensive greedy algorithms. Gluttony search, local search, genetic algorithm (GA), and particle swarm optimization (PSO) were used to compare performance. The proposed parsimonious learning algorithm with MOT outperforms others in most cases. By maintaining a compact architecture, the multioperator-based optimization technique selects the best feasible hyperparameters for the autonomous learning algorithm.
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