A Genetic Programming Approach for Classification of Textures Based on Wavelet Analysis

Chen, Zheng; Lu, Sheng Guo

doi:10.1109/wisp.2007.4447575

Cited by 10 publications

(2 citation statements)

References 8 publications

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“…In this approach, the subpopulations can be totally isolated (in this case they are called islands), or they can exchange their individuals (demes). For example, Chen and Lu [13] used an island subpopulation approach, with the final prediction being decided by majority voting among the different models.…”

Section: Gp For Multiclass Classificationmentioning

confidence: 99%

Progressive Insular Cooperative GP

Rebuli

Vanneschi

2021

Lecture Notes in Computer Science

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This work presents a novel genetic programming system for multi-class classification, called progressively insular cooperative genetic programming (PIC GP). Based on the idea that effective multiclass clas- AQ1sification can be obtained by appropriately joining classifiers that are highly specialized on the single classes, PIC GP evolves, at the same time, two populations. The first population contains individuals called specialists, and each specialist is optimized on one specific target class. The second population contains higher-level individuals, called teams, that join specialists to obtain the final algorithm prediction. By means of three simple parameters, PIC GP can tune the amount of cooperation between specialists of different classes. The first part of the paper is dedicated to a study of the influence of these parameters on the evolution dynamics. The obtained results indicate that PIC GP achieves the best performance when the evolution begins with a high level of cooperation between specialists of different classes, and then this type of cooperation is progressively decreased, until only specialists of the same class can cooperate between each other. The last part of the work is dedicated to an experimental comparison between PIC GP and a set of state-of-theart classification algorithms. The presented results indicate that PIC GP outperforms the majority of its competitors on the studied test problems.

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Section: Gp For Multiclass Classificationmentioning

confidence: 99%

Progressive Insular Cooperative GP

Rebuli

Vanneschi

2021

Lecture Notes in Computer Science

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“…GP has also been successfully used in other applications such as image classification. For example, Chen and Lu [20] proposed a GP framework for texture classification. Their model employed majority voting to increase the performance and obtained an accuracy of 99.6%.…”

Section: Introductionmentioning

confidence: 99%

Genetic Programming Approach to Detect Hate Speech in Social Media

Aljero

Dimililer

2021

IEEE Access

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Social media sites, which became central to our everyday lives, enable users to freely express their opinions, feelings, and ideas due to a certain level of depersonalization and anonymity they provide. If there is no control, these platforms may be used to propagate hate speech. In fact, in recent years, hate speech has increased on social media. Therefore, there is a need to monitor and prevent hate speech on these platforms. However, manual control is not feasible due to the high traffic of content production on social media sites. Moreover, the language used and the length of the messages provide a challenge when using classical machine learning approaches as prediction methods. This paper presents a genetic programming (GP) model for detecting hate speech where each chromosome represents a classifier employing a universal sentence encoder as a feature. A novel mutation technique that affects only the feature values in combination with the standard one-point mutation technique improves the performance of the GP model by enriching the offspring pool with alternative solutions. The proposed GP model outperforms all state-of-the-art systems for the four publicly available hate speech datasets.INDEX TERMS Classification algorithms, genetic programming, machine learning, prediction methods.

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