An evolutionary cut points search for graph clustering-based discretization

Sriwanna, Kittakorn; Boongoen, Tossapon; Iam-On, Natthakan

doi:10.1109/jcsse.2016.7748929

Cited by 2 publications

(3 citation statements)

References 26 publications

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“…e complexity of multidimensional data feature discretization increases sharply with the length of the attribute value interval and the association between attributes [36]. When using the genetic algorithm [43] to optimize the discretization scheme of multidimensional data, the main challenges are as follows.…”

Section: Main Challengesmentioning

confidence: 99%

Reinforcement Learning-Based Genetic Algorithm in Optimizing Multidimensional Data Discretization Scheme

Chen

Huang

et al. 2020

Mathematical Problems in Engineering

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Feature discretization can reduce the complexity of data and improve the efficiency of data mining and machine learning. However, in the process of multidimensional data discretization, limited by the complex correlation among features and the performance bottleneck of traditional discretization criteria, the schemes obtained by most algorithms are not optimal in specific application scenarios and can even fail to meet the accuracy requirements of the system. Although some swarm intelligence algorithms can achieve better results, it is difficult to formulate appropriate strategies without prior knowledge, which will make the search in multidimensional space inefficient, consume many computing resources, and easily fall into local optima. To solve these problems, this paper proposes a genetic algorithm based on reinforcement learning to optimize the discretization scheme of multidimensional data. We use rough sets to construct the individual fitness function, and we design the control function to dynamically adjust population diversity. In addition, we introduce a reinforcement learning mechanism to crossover and mutation to determine the crossover fragments and mutation points of the discretization scheme to be optimized. We conduct simulation experiments on Landsat 8 and Gaofen-2 images, and we compare our method to the traditional genetic algorithm and state-of-the-art discretization methods. Experimental results show that the proposed optimization method can further reduce the number of intervals and simplify the multidimensional dataset without decreasing the data consistency and classification accuracy of discretization.

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Section: Main Challengesmentioning

confidence: 99%

Reinforcement Learning-Based Genetic Algorithm in Optimizing Multidimensional Data Discretization Scheme

Chen

Huang

et al. 2020

Mathematical Problems in Engineering

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“…These problems can be transformed into optimisation problems, in which an optimal set of features, instances or intervals are searched in a finite feature, instance or interval space. These optimisation problems have been tackled by means of heuristics [133], metaheuristics [63,137,163,171,189] and hyper-heuristics [115]. Compared to heuristic-based data preprocessing, metaheuristic-based methods have better global search ability by balancing exploration and exploitation.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Then, from the instance perspective, most optimisation techniques are based on EAs [171], while hyper-heuristics have not been introduced yet, which might be of use similarly to feature selection [115] and improve the generalisation ability of instance selection algorithms. As for discretisation, EAs have been frequently used to search the best set of cut points for each attribute, in which binary encoding was usually utilised to determine whether the predefined cut points were adopted [137,163]. Data balancing can be seen as an instance selection problem, so that, it has been tackled by EAs similarly, in which EAs were used to search for a new set of examples from the imbalanced data by downsampling or oversampling with the purpose of balancing data and achieving good learning performance [63].…”

Section: Data Preprocessingmentioning

confidence: 99%

A review on the self and dual interactions between machine learning and optimisation

2019

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Machine learning and optimisation are two growing fields of artificial intelligence with an enormous number of computer science applications. The techniques in the former area aim to learn knowledge from data or experience, while the techniques from the latter search for the best option or solution to a given problem. To employ these techniques automatically and effectively aligning with the real aim of artificial intelligence, both sets of techniques are frequently hybridised, interacting with each other and themselves. This study focuses on such interactions aiming at (1) presenting a broad overview of the studies on self and dual interactions between machine learning and optimisation; (2) providing a useful tutorial for researchers and practitioners in both fields in support of collaborative work through investigation of the recent advances and analyses of the advantages and disadvantages of different techniques to tackle the same or similar problems; (3) clarifying the overlapping terminologies having different meanings used in both fields; (4) identifying research gaps and potential research directions.

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An evolutionary cut points search for graph clustering-based discretization

Cited by 2 publications

References 26 publications

Reinforcement Learning-Based Genetic Algorithm in Optimizing Multidimensional Data Discretization Scheme

Reinforcement Learning-Based Genetic Algorithm in Optimizing Multidimensional Data Discretization Scheme

A review on the self and dual interactions between machine learning and optimisation

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