Cited By
View all- Nguyen BXue BBrowne WZhang M(2023)Evolutionary ClassificationHandbook of Evolutionary Machine Learning10.1007/978-981-99-3814-8_7(171-204)Online publication date: 2-Nov-2023
Accelerated pattern search with variable solution size for simultaneous instance selection and generation
Pages 256 - 259
Abstract
The search for the optimum in a mixed continuous-combinatorial space is a challenging task since it requires operators that handle both natures of the search domain. Instance reduction (IR), an important pre-processing technique in data science, is often performed in separated stages, combining instance selection (IS) first, and sub-sequently instance generation (IG). This paper investigates a fast optimisation approach for IR considering the two stages at once. This approach, namely Accelerated Pattern Search with Variable Solution Size (APS-VSS), is characterised by a variable solution size, an accelerated objective function computation, and a single-point memetic structure designed for IG.
APS-VSS is composed of a global search crossover and three local searches (LS). The global operator prevents premature convergence to local optima, whilst the three LS algorithms optimise the reduced set (RS). Furthermore, by using the k-nearest neighbours algorithm as a base classifier, APS-VSS exploits the search logic of the LS to accelerate, by orders of magnitude, objective function computation. The experiments show that APS-VSS outperforms existing algorithms using the single-point structure, and is statistically as competitive as state-of-the-art IR techniques regarding accuracy and reduction rates, while reducing significantly the runtime.
References
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J. R. Cano, F. Herrera, and M. Lozano. 2003. Using Evolutionary Algorithms as Instance Selection for Data reduction in KDD: an experimental study. IEEE Transactions on Evolutionary Computation 7, 6 (Dec 2003), 561--575.
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T. M. Cover and P. E. Hart. 1967. Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory 13, 1 (1967), 21--27.
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S. García, J. R. Cano, and F. Herrera. 2008. A Memetic Algorithm for Evolutionary Prototype Selection: A Scaling up Approach. Pattern Recognition 41, 8 (2008), 2693--2709.
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S. García, J. Derrac, J.R. Cano, and F. Herrera. 2012. Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 3 (2012), 417--435.
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H. L. Le, F. Neri, and I. Triguero. 2021. SPMS-ALS: A Single-Point Memetic structure with accelerated local search for instance reduction. Swarm and Evolutionary Computation (2021), 100991.
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F. Neri and I. Triguero. 2020. A Local Search with a Surrogate Assisted Option for Instance Reduction. In Applications of Evolutionary Computation (Lecture Notes in Computer Science, Vol. 12104), Pedro A. Castillo, Juan Luis Jiménez Laredo, and Francisco Fernández de Vega (Eds.). Springer, 578--594.
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R. Tanabe and A. S. Fukunaga. 2014. Improving the Search Performance of SHADE Using Linear Population Size Reduction. In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 1658--1665.
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I. Triguero, J. Derrac, S. García, and F. Herrera. 2012. A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification. IEEE Transactions on Systems, Man, and Cybernetics-Part C 42, 1 (2012), 86--100.
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Published: 19 July 2022
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View all- Nguyen BXue BBrowne WZhang M(2023)Evolutionary ClassificationHandbook of Evolutionary Machine Learning10.1007/978-981-99-3814-8_7(171-204)Online publication date: 2-Nov-2023
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