Comparative Analysis of Decision Tree Algorithm for Learning Ordinal Data Expressed as Pairwise Comparisons

Qomariyah, Nunung Nurul; Heriyanni, Eileen; Fajar, Ahmad Nurul; Kazakov, Dimitar

doi:10.1109/icoict49345.2020.9166341

Cited by 14 publications

(3 citation statements)

References 6 publications

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“…It is used to represent decisions and decision-making explicitly [28]. As the name suggests, DT is a tree-based model characterized by its simplicity in understanding decisions and the ability to select the most preferential features [29]. In addition, it can classify data without vast calculations [30].…”

Section: Learning Phasementioning

confidence: 99%

Architecting a machine learning pipeline for online traffic classification in software defined networking using spark

Samaan

Jeiad

2023

IJ-AI

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Precise traffic classification is essential to numerous network functionalities such as routing, network management, and resource allocation. Traditional classification techniques became insufficient due to the massive growth of network traffic that requires high computational costs. The arising model of software defined networking (SDN) has adjusted the network architecture to get a centralized controller that preserves a global view over the entire network. This paper proposes a model for SDN traffic classification based on machine learning (ML) using the Spark framework. The proposed model consists of two phases; learning and deployment. A ML pipeline is constructed in the learning phase, consisting of a set of stages combined as a single entity. Three ML models are built and evaluated; decision tree, random forest, and logistic regression, for classifying a well-known 75 applications, including Google and YouTube, accurately and in a short time scale. A dataset consisting of 3,577,296 flows with 87 features is used for training and testing the models. The decision tree model is elected for deployment according to the performance results, which indicate that it has the best accuracy with 0.98. The performance of the proposed model is compared with the state-of-the-art works, and better accuracy result is reported.

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Section: Learning Phasementioning

confidence: 99%

Architecting a machine learning pipeline for online traffic classification in software defined networking using spark

Samaan

Jeiad

2023

IJ-AI

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“…In the field of machine learning, there has been a lot of research on classification problems. Qomariyah et al (2020) showed the review of decision tree implementation for learning user preferences data expressed in pairwise comparisons. The authors used a 10-fold cross-validation and hold-out technique to evaluate the performance of four different decision tree algorithms.…”

Section: Introductionmentioning

confidence: 99%

User Consumption Behavior Recognition Based on SMOTE and Improved AdaBoost

Zhu

Tao

et al. 2022

International Journal of Software Science and Computational Intelligence

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The sudden outbreak of COVID-19 has dealt a huge blow to traditional education and training companies. Institutions use the WeChat platform to attract users, but how to identify high-quality users has always been a difficult point for enterprises. In this paper, researchers proposed a classification algorithm based on SMOTE and the improved AdaBoost, which fuses feature information weights and sample weights to effectively solve the problems of overfitting and sample imbalance. To justify the study, it was compared with other traditional machine-learning algorithms. The accuracy and recall of the model increased by 19% and 36%, respectively, and the AUC value reached 0.98, indicating that the model could effectively identify the user's purchase intention. The proposed algorithm also ensures that it works well in spam identification and fraud detection. This research is of great significance for educational institutions to identify high-quality users of the WeChat platform and increase purchase conversion rate.

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“…Algoritma CART merupakan algoritma pembelajaran populer dalam Supervised Machine Learning [13]. Algoritma CART merupakan algoritma Decision Tree Learning yang dapat digunakan untuk melakukan klasifikasi ataupun regresi.…”

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Model Deteksi Botnet Menggunakan Algoritma Decision Tree Dengan Untuk Mengidentifikasi Serangan Click Fraud

Firdaus,

Hadiana,

Kasyidi

2023

j_ict

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Malicious Software (Malware) merupakan program yang dibuat khusus untuk merugikan orang lain. Salah satunya Botnet, di mana Botnet dapat menginfeksi perangkat komputer serta membuat komputer tersebut sebagai suatu alat yang nantinya akan dikendalikan secara paksa oleh pemilik dari program Malware tersebut. Botnet sendiri dapat melakukan serangan Click Fraud untuk melakukan Fake Clicks terhadap iklan yang bersifat Pay Per Click. Botnet dengan serangan Click Fraud memiliki pola tingkah laku yang dapat diklasifikasikan dengan menggunakan Dataset CTU-13. Sehingga Flow Traffic dari Botnet yang melakukan serangan Click Fraud akan dapat terdeteksi dengan menggunakan algoritma CART dengan menggunakan teknik SMOTE untuk melakukan Oversampling dan teknik Random Undersampling untuk menangati ketidakseimbangan sebaran data untuk setiap kelasnya. Dengan menggunakan rasio Undersampling yaitu 50% dan terdapat 2 skenario untuk penggunaan teknik SMOTE, yaitu sebelum dan setelah data dibagi menjadi data latih dan data uji. Berdasarkan dari hasil penelitian yang telah dilakukan dapat disimpulkan bahwa dengan penggunaan teknik SMOTE dan Random Undersampling dalam kasus untuk pendeteksian Botnet yang melakukan serangan Click Fraud sebelum membagi dataset menjadi data latih dan data uji dapat meningkatkan akurasi ataupun kinerja dari model tersebut dengan mencapai tingkat akurasi sebesar 99.97%. Dan Nilai F-Score dari model yang menggunakan SMOTE dan Random Undersampling adalah 99.96%.

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Comparative Analysis of Decision Tree Algorithm for Learning Ordinal Data Expressed as Pairwise Comparisons

Cited by 14 publications

References 6 publications

Architecting a machine learning pipeline for online traffic classification in software defined networking using spark

Architecting a machine learning pipeline for online traffic classification in software defined networking using spark

User Consumption Behavior Recognition Based on SMOTE and Improved AdaBoost

Model Deteksi Botnet Menggunakan Algoritma Decision Tree Dengan Untuk Mengidentifikasi Serangan Click Fraud

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