Automatic Mining of Numerical Classification Rules with Parliamentary Optimization Algorithm

Kiziloluk, Soner; Alataş, Bilal

doi:10.4316/aece.2015.04003

Cited by 17 publications

(9 citation statements)

References 11 publications

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“…Accuracy adaptive fuzzy [34] 89.80 Hybrid SVR [35] 86.13 Rule mining with POA [36] 79.06 Modified PSO [37] 85.19 Re-RX with J48graft [38] 83.83 Re-RX with C4.5 [38] 80.83 QFAM-GA [39] 91.91 DNN-SAE [40] 86.26 SM-RuleMiner [14] 89.87 Proposed Model 96.09…”

Section: Methodsmentioning

confidence: 99%

ENN Ensemble based Neural Network method for Diabetes Classification

Kumari,

2020

IJEAT

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Diabetes is considered as one of the most chronic disease which has serious impact on human health and leading cause of mortality worldwide. The early prediction of diabetes can help clinicians to provide a better diagnosis to the patients. Recently, computed aided diagnosis systems have gained attention due to significant growth in data mining, and machine learning. Several approaches are present based on the machine learning techniques but due to poor classification performance and computational complexity, it becomes difficult to utilize for real-time applications. Ensemble classification approaches have reported a noteworthy improvement in diabetes classification but desired accuracy is still a challenging task. Hence, in this work we introduce a combined hybrid approach called as ENN-Ensemble based neural network approach for diabetes classification. In this approach, a feature selection process is presented using neighboring search technique; the selected features are processed through the feature ranking model to generate the efficient feature subset for better classification accuracy. Finally, these features are learned and classified using neural network classifier. The experimental study shows that the proposed approach achieves better accuracy when compared with the existing techniques.

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Section: Methodsmentioning

confidence: 99%

ENN Ensemble based Neural Network method for Diabetes Classification

Kumari,

2020

IJEAT

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“…One of the most important data mining techniques for IDSs is the rule discovery which tries to locate a collection of rules that can recognize the specific class from various groups of classes. Such a rule discovery tool is an essential phase for the classification tasks (Kiziloluk & Alatas, 2015;Patel & Buddhadev, 2015 (Aghdam & Kabiri, 2016;Varma, 2016).…”

Section: Introductionmentioning

confidence: 99%

Rule Generation Based on Modified Cuttlefish Algorithm for Intrusion Detection System

Eesa¹,

sadiq²,

Hassan³

et al. 2021

Uludağ University Journal of the Faculty of Engineering

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Nowadays, with the rapid prevalence of networked machines and Internet technologies, intrusion detection systems are increasingly in demand. Consequently, numerous illicit activities by external and internal attackers need to be detected. Thus, earlier detection of such activities is necessary for protecting data and information. In this paper, we investigated the use of the Cuttlefish optimization algorithm as a new rule generation method for the classification task to deal with the intrusion detection problem. The effectiveness of the proposed method was tested using KDD Cup 99 dataset based on different evaluation methods. The obtained results were also compared with the results obtained by some classical well-known algorithms namely Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighborhood (K-NN). Our experimental results showed that the proposed method demonstrates a good classification performance and provides significantly preferable results when compared with the performance of other traditional algorithms. The proposed method produced 93.9%, 92.2%, and 94.7% in terms of precision, recall, and area under curve, respectively.

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“…By contrast, metaheuristic algorithms are more efficient in overcoming these challenges. Many metaheuristic algorithms are inspired by biological phenomena as well as by physical, social, and chemical processes [1]. For example, the genetic algorithm (GA) [2] and artificial immune systems (AISs) [3] are based on biology, the gravitational search algorithm (GSA) [4] is based on physics, the imperialist competitive algorithm (ICA) [5] is based on social concepts, and the artificial chemical reaction optimization algorithm (ACROA) [6] is based on chemistry.…”

Section: Introductionmentioning

confidence: 99%

“…Only a few studies have investigated the POA. In these studies, the POA was used for different problems, such as global optimization [18], permutation constraint satisfaction problems [19], overlapping community detection in social networks [20], finding numerical classification rules [1], and classification of Web pages [21]. Furthermore, a hybrid version of the POA, i.e.…”

Section: Introductionmentioning

confidence: 99%

Hybrid parliamentary optimization and big bang-big crunch algorithm for globaloptimization

Kiziloluk¹,

Özer²

2019

Turk J Elec Eng & Comp Sci

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Researchers have developed different metaheuristic algorithms to solve various optimization problems. The efficiency of a metaheuristic algorithm depends on the balance between exploration and exploitation. This paper presents the hybrid parliamentary optimization and big bang-big crunch (HPO-BBBC) algorithm, which is a combination of the parliamentary optimization algorithm (POA) and the big bang-big crunch (BB-BC) optimization algorithm. The intragroup competition phase of the POA is a process that searches for potential points in the search space, thereby providing an exploration mechanism. By contrast, the BB-BC algorithm has an effective exploitation mechanism. In the proposed method, steps of the BB-BC algorithm are added to the intragroup competition phase of the POA in order to improve the exploitation capabilities of the POA. Thus, the proposed method achieves a good balance between exploration and exploitation. The performance of the HPO-BBBC algorithm was tested using well-known mathematical test functions and compared with that of the POA, the BB-BC algorithm, and some other metaheuristics, namely the genetic algorithm, multiverse optimizer, crow search algorithm, dragonfly algorithm, and moth-flame optimization algorithm. The HPO-BBBC algorithm was found to achieve better optimization performance and a higher convergence speed than the above-mentioned algorithms on most benchmark problems.

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Automatic Mining of Numerical Classification Rules with Parliamentary Optimization Algorithm

Cited by 17 publications

References 11 publications

ENN Ensemble based Neural Network method for Diabetes Classification

ENN Ensemble based Neural Network method for Diabetes Classification

Rule Generation Based on Modified Cuttlefish Algorithm for Intrusion Detection System

Hybrid parliamentary optimization and big bang-big crunch algorithm for globaloptimization

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