Graphic object feature extraction system based on Cuckoo Search Algorithm

Woźniak, Marcin; Połap, Dawid; Napoli, Christian; Tramontana, Emiliano

doi:10.1016/j.eswa.2016.08.068

Cited by 23 publications

(11 citation statements)

References 19 publications

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“…In the other hand, several novel nature inspired optimization algorithms for feature selection are proposed, for example: Cuckoo search algorithm [31], grey wolf optimizer, ant lion optimization [17], crow search and cuttlefish algorithm. These new methods provide new ideas for the development of feature selection.…”

Section: Literature Review For Feature Selectionmentioning

confidence: 99%

Feature Selection Using Improved Forest Optimization Algorithm

Xie

Geng-guo²,

Zhang³

et al. 2020

ITC

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Feature selection is one of the hottest topics in the field of machine learning and data mining. In 2016, the feature selection using forest optimization algorithm (FSFOA) was proposed, which had a better classification performance and dimensionality reduction ability. However, there are some shortcomings in FSFOA. Feature Selection using Improved Forest Optimization Algorithm (FSIFOA) is proposed in this article, which aims at solving the problems of FSFOA during the stages of random initialization, forming the candidate population and updating the best tree. FSIFOA uses the Pearson correlation coefficient and the L1 regularization method to replace the random initialization strategy in the initialization stage, uses the method of separating good and bad trees and filling the quantity gap between them to solve the problem of category imbalance in the candidate population generation stage, adds trees of the same precision but different dimension compared with the best tree to the forest in the update stage. In experiment, the new algorithm uses the same data and parameters as the traditional algorithm to test the small, medium and large dimensional data respectively. The results of the experiments show that the new algorithm can improve the classification accuracy of classifiers and increase the dimension reduction ratio compared with the traditional algorithms in the medium and large dimension data set.

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Section: Literature Review For Feature Selectionmentioning

confidence: 99%

Feature Selection Using Improved Forest Optimization Algorithm

Xie

Geng-guo²,

Zhang³

et al. 2020

ITC

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“…Domain-specific languages are successfully used in different areas, for example, application logging [13], acceptance tests [14], graphic shape description [15], expert systems [16], text analysis [17], or automatic assessment of students [18]. Source code annotations can also be considered a DSLs [19].…”

Section: Related Workmentioning

confidence: 99%

Abstract-Syntax-Driven Development of Oberon-0 Using YAJCo

Chodarev

Bacikova

2019

JIOS

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YAJCo is a tool for the development of software languages based on an annotated language model. The model is represented by Java classes with annotations defining their mapping to concrete syntax. This approach to language definition enables the abstract syntax to be the central point of the development process, instead of concrete syntax. In this paper, a case study of Oberon-0 programming language development is presented. The study is based on the LTDA Tool Challenge and showcases details of abstract and concrete syntax definition using YAJCo, as well as the implementation of name resolution, type checking, model transformation, and code generation. The language was implemented in a modular fashion to demonstrate language extension mechanisms supported by YAJCo.

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“…Cuckoo search has been used to carry out clustering of web search results [10], medical data classification [43], twitter sentiment analysis [51], email spam classification [33], multi-document summarization [57] and information granule formation [59]. In addition, CS has also been used to do feature extraction for graphic objects [76]. A set of applications in life science data mining were carried out by Fong et al [22] with various swarm techniques.…”

Section: Data Miningmentioning

confidence: 99%