Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

Nagra, Arfan Ali; Han, Fei; Ling, Qing; Abubaker, Muhammad; Ahmad, Farooq; Mehta, Sumet; Apasiba, Abeo Timothy

doi:10.1080/09540091.2019.1609419

Cited by 29 publications

(13 citation statements)

References 45 publications

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“…Therefore, the average values of accuracy obtained for KMC images are 99.61% and for the RIM-ONE database, the obtained average values of accuracy are 99.15%. Nagra et al [80] introduced the Self-Inertia Weight Adaptive Particle Swarm Optimization with Gradient Base Local Search (SIW-APSO-LS) feature selection approach was modified to conduct feature selection and the C4.5 decision tree method was used as a classifier to determine the sub-sets of features given. When comparing algorithms in feature selection problems, 16 datasets from the UCI Machine Learning Repository were used for the experiments.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification Based on Decision Tree Algorithm for Machine Learning

Charbuty

Abdulazeez

2021

JASTT

893

369

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Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending a decision tree from available data, such as machine study, pattern recognition, and statistics. In various fields such as medical disease analysis, text classification, user smartphone classification, images, and many more the employment of Decision tree classifiers has been proposed in many ways. This paper provides a detailed approach to the decision trees. Furthermore, paper specifics, such as algorithms/approaches used, datasets, and outcomes achieved, are evaluated and outlined comprehensively. In addition, all of the approaches analyzed were discussed to illustrate the themes of the authors and identify the most accurate classifiers. As a result, the uses of different types of datasets are discussed and their findings are analyzed.

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Section: Literature Reviewmentioning

confidence: 99%

Classification Based on Decision Tree Algorithm for Machine Learning

Charbuty

Abdulazeez

2021

JASTT

893

369

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“…There are many data mining methods that can be used for credit debt overdue estimation models construction. This work is focused on three most effective and frequently used methods: a decision tree (Nagra et al, 2020;Ke et al, 2017), а logistic regression (Ansori et al, 2019;Meier et al, 2008;Asar & Wu, 2020) and a neural network (Ismagilov et al, 2018;Mustafin et al, 2018;Swiderski et al, 2012;. When using decision trees to classify loan applications, a set of rules is applied, which is formed when constructing a tree based on a training set (Alqam & Zaro, 2019).…”

Section: Methodsmentioning

confidence: 99%

Collection Scoring Models Development and Research Based on the Deductor Analytical Platform

Ismagilov¹,

Sabirova²,

Kataseva³

et al. 2020

Nexo Revista Científica

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This article solves the problem of collection scoring models constructing and researching. The relevance of solving this problem on the intelligent modeling technologies basis: decision trees, logistic regression and neural networks is noted. The initial data for the models was a set of 14 columns and 5779 rows. The models construction was performed in Deductor platform. Each model was tested on the set of 462 records. For all models, the corresponding classification matrix were constructed and the1st and 2nd kind errors were calculated, as well as the general error of the models. In terms of minimizing these errors, logistic regression showed the worst results, and the neural network showed the best. In addition, the constructed models effectiveness was evaluated according to «income» and «time» criteria. By the time costs the logistic regression model exceeds other models. However, in terms of income the neural network model was the best. Thus, the results showed that in order to minimize the time spent on work with debtors it is advisable to use a logistic model. However, to maximize profits and minimize classification errors, it is appropriate to use a neural network model. This indicates its effectiveness and practical use possibility in intelligent scoring systems.

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“…In this work, a neural network, a decision tree, and linear regression are used as tools for constructing bots identification models in social networks [17][18][19]. The choice of these methods is due to their high efficiency in solving problems of diagnostics, pattern recognition, objects classification, and their condition assessment [20][21][22][23].…”

Section: Methodsmentioning

confidence: 99%