Classification of multiclass imbalanced data using cost-sensitive decision tree C5.0

Febriantono, M. Aldiki; Pramono, Sholeh Hadi; Rahmadwati, Rahmadwati; Naghdy, Golshah

doi:10.11591/ijai.v9.i1.pp65-72

Cited by 19 publications

(16 citation statements)

References 18 publications

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“…Within this context, the six ML techniques used in this paper are It should be noted that although there exists an enhanced release of C4.5 algorithm, called C5.0 algorithm, the researchers sticked with the C4.5 because it is freely available unlike the enhanced version. Moreover, it is revealed that C4.5 can still produce somehow equal or better performance compared with C5.0 [37,38].…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions

2022

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This paper compares classification performances of machine learning (ML) techniques for forecasting dispute resolutions in construction projects, thereby mitigating the impacts of potential disputes. Findings revealed that resolution cost and duration, contractor type, dispute source, and occurrence of changes were the most influential factors on dispute resolution method (DRM) preferences. The promising accuracy of the majority voting classifier (89.44%) indicates that the proposed model can provide decision-support in identification of potential resolutions. Decision-makers can avoid unsatisfactory processes using these forecasts. This paper demonstrated the effectiveness of ML techniques in classification of DRMs, and the proposed prediction model outperformed previous studies.

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Section: Machine Learning Techniquesmentioning

confidence: 99%

Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions

2022

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“…The initial inference process of C4.5 utilizes the decision-making tree in common [ 57 ]. The quantified discrete function in C4.5 is used by using a decision tree to reply to the learnt function.…”

Section: Background and Related Workmentioning

confidence: 99%

Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network

Prabakaran

Ramalakshmi

Hussain

et al. 2022

Sensors

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Decoupled data and control planes in Software Defined Networks (SDN) allow them to handle an increasing number of threats by limiting harmful network links at the switching stage. As storage, high-end servers, and network devices, Network Function Virtualization (NFV) is designed to replace purpose-built network elements with VNFs (Virtualized Network Functions). A Software Defined Network Function Virtualization (SDNFV) network is designed in this paper to boost network performance. Stateful firewall services are deployed as VNFs in the SDN network in this article to offer security and boost network scalability. The SDN controller’s role is to develop a set of guidelines and rules to avoid hazardous network connectivity. Intruder assaults that employ numerous socket addresses cannot be adequately protected by these strategies. Machine learning algorithms are trained using traditional network threat intelligence data to identify potentially malicious linkages and probable attack targets. Based on conventional network data (DT), Bayesian Network (BayesNet), Naive-Bayes, C4.5, and Decision Table (DT) algorithms are used to predict the target host that will be attacked. The experimental results shows that the Bayesian Network algorithm achieved an average prediction accuracy of 92.87%, Native–Bayes Algorithm achieved an average prediction accuracy of 87.81%, C4.5 Algorithm achieved an average prediction accuracy of 84.92%, and the Decision Tree algorithm achieved an average prediction accuracy of 83.18%. There were 451 k login attempts from 178 different countries, with over 70 k source IP addresses and 40 k source port addresses recorded in a large dataset from nine honeypot servers.

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“…Febriantono et al [ 31 ] applied decision tree C5.0 of cost-sensitive type to work out imbalanced data issue of multiclass nature. At the first step, C5.0 algorithm was utilized by the decision tree model.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Hybrid Approach for Handling Class Imbalance Problem

Desuky

Hussain

2021

Arab J Sci Eng

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Class imbalance issue that presents in many real-world datasets exhibit favouritism toward the majority class and showcases poor performance for the minority class. Such misclassifications may incur dubious outcome in case of disease diagnosis and other critical applications. Hence, it is a hot topic for the researchers to tackle the class imbalance issue. We present a novel hybrid approach for handling such datasets. We utilize simulated annealing algorithm for undersampling and apply support vector machine, decision tree, k-nearest neighbor and discriminant analysis for the classification task. We validate our technique in 51 real-world datasets and compare it with other recent works. Our technique yields better efficacy than the existing techniques and hence it can be applied in imbalance datasets to mitigate the misclassification.

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Classification of multiclass imbalanced data using cost-sensitive decision tree C5.0

Cited by 19 publications

References 18 publications

Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions

Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions

Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network

An Improved Hybrid Approach for Handling Class Imbalance Problem

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