A New Approach Based on Bat Algorithm for Inducing Optimal Decision Trees Classifiers

Bida, Ikram; Aouat, Saliha

doi:10.1007/978-3-030-03577-8_69

Cited by 4 publications

(1 citation statement)

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“…The SI applications in those two aspects are mainly related to parameter tuning. For classification, works can be found in literature that combine SI algorithms with regression model [ 53 ], support vector machine [ 7 , 14 , 60 ], k-nearest neighbor classifiers [ 58 , 65 ], Decision trees [ 3 , 35 ], as well as the neural networks [ 30 , 62 ]. For clustering, some recent works are related to utilizing SI with k-means [ 28 , 59 , 61 ], c-means [ 21 ], and other linear or non-linear clustering algorithms [ 19 , 27 ].…”

Section: Theoretical Applicationsmentioning

confidence: 99%

Swarm Intelligence in Data Science: Applications, Opportunities and Challenges

Yang

Shen

et al. 2020

Lecture Notes in Computer Science

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The Swarm Intelligence (SI) algorithms have been proved to be a comprehensive method to solve complex optimization problems by simulating the emergence behaviors of biological swarms. Nowadays, data science is getting more and more attention, which needs quick management and analysis of massive data. Most traditional methods can only be applied to continuous and differentiable functions. As a set of population-based approaches, it is proven by some recent research works that the SI algorithms have great potential for relevant tasks in this field. In order to gather better insight into the utilization of these methods in data science and to provide a further reference for future researches, this paper focuses on the relationship between data science and swarm intelligence. After introducing the mainstream swarm intelligence algorithms and their common characteristics, both the theoretical and real-world applications in the literature which utilize the swarm intelligence to the related domains of data analytics are reviewed. Based on the summary of the existing works, this paper also analyzes the opportunities and challenges in this field, which attempts to shed some light on designing more effective algorithms to solve the problems in data science for real-world applications.

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