Swarm Intelligence-based Decision Trees Induction for Classification — A Brief Analysis

Bida, Ikram; Aouat, Saliha

doi:10.1109/ihsh51661.2021.9378746

Cited by 4 publications

(6 citation statements)

References 26 publications

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“…At the end of each branch is the terminal node, denoted by leaves, which represent the most appropriate class for the rule (Azevedo et al, 2019). The DT goal is to create a tree model that covers most of the data set and can predict a class by learning simple rules deduced from training data instances (Bida & Aouat, 2021). Several heuristic-based algorithms have been developed to automatically induce DTs and improve the classifier performance, such as Iterative Dichotomiser 3 (ID3), C4.5 Algorithm (C4.5), Classification and Regression Trees (CART), Chi-square Automatic Interaction Detector (CHAID), Quaternion Estimation Algorithm (QUEST).…”

Section: Supervised Classification Methodsmentioning

confidence: 99%

“…Several heuristic-based algorithms have been developed to automatically induce DTs and improve the classifier performance, such as Iterative Dichotomiser 3 (ID3), C4.5 Algorithm (C4.5), Classification and Regression Trees (CART), Chi-square Automatic Interaction Detector (CHAID), Quaternion Estimation Algorithm (QUEST). However, these heuristics suffer when exposed to local optima ambush, producing a tree that is not guaranteed to be the global optima (Bida & Aouat, 2021).…”

Section: Supervised Classification Methodsmentioning

confidence: 99%

“…To overcome this challenge, some authors combined DT-induced techniques with bioinspired techniques. Bida and Aouat (2021) described some swarm intelligent DT induction algorithms based on ACO, PSO and BA. Jariyavajee et al ( 2019) improved the DT performance by Joint Approximation Diagonalization of Eigen-matrices algorithm (JADE), which is based on Differential Evolution strategies.…”

Section: Supervised Classification Methodsmentioning

confidence: 99%

“…Decision tree & ACO, PSO, and BA (Bida & Aouat, 2021) Differential evolution (Jariyavajee et al, 2019) GA (Adibi, 2019;Damanik et al, 2019) PSO (Nagra et al, 2020;Agustina et al, 2019) Salp swarm optimization (Sarala et al, 2021) Classification Error rate minimization. Accuracy improvement.…”

Section: Optimum Number Of Centroids Fuzzy C-meansmentioning

confidence: 99%

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Hybrid approaches to optimization and machine learning methods: a systematic literature review

Azevedo,

Rocha,

Pereira

2024

Mach Learn

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Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.

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Section: Supervised Classification Methodsmentioning

confidence: 99%

Section: Supervised Classification Methodsmentioning

confidence: 99%

Section: Supervised Classification Methodsmentioning

confidence: 99%

Section: Optimum Number Of Centroids Fuzzy C-meansmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Azevedo,

Rocha,

Pereira

2024

Mach Learn

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“…Growing in encoding complexity, SI algorithms instead may encode trees with pairs of vectors, each vector encoding different characteristics of each node. As per variation operators, SI algorithms enjoy a large plethora of candidate optimizers: ant colony (Bursa et al 2008), particle swarm, and bat swarm (Bida and Aouat 2021), each directly inspired by typical natural or animal ecosystems.…”

Section: Related Workmentioning

confidence: 99%

Generative Model for Decision Trees

Guidotti,

Monreale,

Setzu

et al. 2024

AAAI

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Decision trees are among the most popular supervised models due to their interpretability and knowledge representation resembling human reasoning. Commonly-used decision tree induction algorithms are based on greedy top-down strategies. Although these approaches are known to be an efficient heuristic, the resulting trees are only locally optimal and tend to have overly complex structures. On the other hand, optimal decision tree algorithms attempt to create an entire decision tree at once to achieve global optimality. We place our proposal between these approaches by designing a generative model for decision trees. Our method first learns a latent decision tree space through a variational architecture using pre-trained decision tree models. Then, it adopts a genetic procedure to explore such latent space to find a compact decision tree with good predictive performance. We compare our proposal against classical tree induction methods, optimal approaches, and ensemble models. The results show that our proposal can generate accurate and shallow, i.e., interpretable, decision trees.

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Enhanced Ant Colony System for Water Quality Rules Classification in Smart Aquaculture

Munif,

Nasir,

Ahmad

2024

2024 IEEE Symposium on Wireless Technology &Amp;amp; Applications (ISWTA)

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Swarm Intelligence-based Decision Trees Induction for Classification — A Brief Analysis

Cited by 4 publications

References 26 publications

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Generative Model for Decision Trees

Enhanced Ant Colony System for Water Quality Rules Classification in Smart Aquaculture

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