Prediction performance of improved decision tree-based algorithms: a review

Mienye, Ibomoiye Domor; Sun, Yanxia; Wang, Zenghui

doi:10.1016/j.promfg.2019.06.011

Cited by 127 publications

(70 citation statements)

References 13 publications

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“…It is important to underline that the decision tree uses the same number of random features of the training sample to create a random forest consisting of different decision trees. The decision tree is based on CART, ID3, and C4.5 algorithms [21]. Training is performed in order to get two the most homogeneous subsamples for each root.…”

Section: The Algorithm Of Random Forestmentioning

confidence: 99%

DoS attacks detection in MQTT networks

Dikii

Arustamov

Grishentsev

2021

IJEECS

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<span>The paper considers the problem of protecting the Internet of things infrastructure against denial-of-service (DoS) attacks at the application level. The authors considered parameters that affect the network gateway workload: message frequency, payload size, number of recipients and some others. We proposed a modular structure of the attack detection tool presented by three classifiers that use the following attributes: username, device ID, and IP-address. The following types of classifiers have been the objects for the research: multilayer perceptron, random forest algorithm, and modifications of the support vector machine. Some scenarios for the behavior of network devices have been simulated. It was proved that for the proposed feature vector on simulated training and test data sets, the best results have been shown by a multilayer perceptron and a support vector machine with a radial basis function of the kernel and optimization with SMO algorithm. The authors also determined the conditions under which the selected classifiers have the best quality of recognizing abnormal and legitimate traffic in MQTT networks. </span>

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Section: The Algorithm Of Random Forestmentioning

confidence: 99%

DoS attacks detection in MQTT networks

Dikii

Arustamov

Grishentsev

2021

IJEECS

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“…It is one of the techniques of Machine Learning the construction of which is aiming at, the sequential split of a set of observations into subsets. It is easily apprehended by people because it applies simple representations for the classification of examples, and it is often used in the mining data sector [16]. The basic purpose of the Decision Trees is the prediction of the variable value that is trained by data created by other independent variables [17].…”

Section: Decision Treesmentioning

confidence: 99%

Optimization of the Procedures for Checking the Functionality of the Greek Railways: Data Mining and Machine Learning Approach to Predict Passenger Train Immobilization

Kalathas¹,

Papoutsidakis²,

Drosos³

2020

Adv. sci. technol. eng. syst. j.

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Information is the driving force of businesses because it can ensure the ability of knowledge and prediction. The railway industry produces a huge ume of data, with the proper processing of them and the use of innovative technology, there is the possibility of beneficial information to be provided which constitute the deciding factor for the correct decision making. Safety is the railway comparative advantage that has to be reinforced by each business administration while making the optimum decisions. The main purpose of this paper is the investigation of the most important dysfunctions that arise in a train and can cause its immobilization at the main passenger rail, resulting in huge delays of conducting the routes setting the passengers at risk. Afterwards the total of malfunctions is assessed and the most important, potentially, malfunction is assessed, so as the executives of the Greek Railway company to plan and redefine the processes and the initial plan of the predictive maintenance. This paper demonstrates the effort of implementing innovative applications by making use of methods from the rapidly developed field of Data Mining to the Greek Railway Company that uses obsolete procedures for the control of the trains' functionality in order to investigate the data for the provision of specialized information which will be used as a tool for the faster, more accurate and precise decision making. This decision making approach is based on a specific algorithm's design in order to automatically detect faults and make periodic maintenance of trains easier. Holistic approach is performed in the management of real data from the Greek railway industry and a predictive model of Machine Learning is developed, for the optimization of the management's performance of the trains reinforcing the strategic target of the railway industry which is the transportation of citizens with safety and comfort.

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“…Pohon keputusan algoritma C4.5 digunakan untuk penalaran tegas manajemen tanggap darurat [5], kontruksi kendala otomatis untuk model Mixed-Integer Linear Programming (MILP) dari data [6] dan pengembangan pohon keputusan [7]. Algoritma ini juga digunakan oleh [8] untuk penilaian stabilitas tegangan online dan hasilnya bisa membantu operator sistem menilai status stabilitas tegangan secara real-time.…”

Section: Pendahuluanunclassified

“…Pohon keputusan memiliki fungsi untuk menyelidiki data, mencari hubungan tersembunyi antara calon variabel target dengan variabel input yang akan digunakan. Perpaduan antara pemodelan dan eksplorasi data pada pohon keputusan sangat direkomendasikan sebagai langkah awal pemodelan serta model akhir dari beberapa teknik lain [7]. Simpul node dari decission tree pohon dibedakan menjadi tiga [11], yaitu : a. Simpul akar (root node) yang letaknya berada pada bagian teratas serta tidak memiliki inputan namun terkadang memiliki output lebih dari satu atau tidak sama sekali.…”

Section: Splitinfo(sa)unclassified

Prediksi Penyakit Demam Berdarah Di Puskesmas Ngemplak Simongan Menggunakan Algoritma C4.5

2020

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Dengue Hemorrhagic Fever (DHF) is a disease whose main cause is the flaviviridae virus. This virus can be transmitted through mosquito bites. The spread of this disease is faster in urban areas than in rural areas due to the high population density. Aedes aegypti mosquito is very easy to spread dengue virus from one person to another because it has a domestic nature. The Ministry of Health has collaborated with local health centers in the DHF prevention program. Ngemplak Simongan Health Center is one of the public health centers located in the District of West Semarang that serves a variety of treatments for this type of disease, one of which is a patient with Dengue Hemorrhagic Fever (DHF). C4.5 algorithm is used to predict dengue fever which aims to produce a decision tree. The choice of using Algortima is because it is widely used to describe a pattern / knowledge / information in the form of a decision tree explicitly. Application created using PHP programming language that produces prediction of dengue fever. The test results obtained an accuracy value of 94.44% so that the application program built can be used correctly.

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Prediction performance of improved decision tree-based algorithms: a review

Cited by 127 publications

References 13 publications

DoS attacks detection in MQTT networks

DoS attacks detection in MQTT networks

Optimization of the Procedures for Checking the Functionality of the Greek Railways: Data Mining and Machine Learning Approach to Predict Passenger Train Immobilization

Prediksi Penyakit Demam Berdarah Di Puskesmas Ngemplak Simongan Menggunakan Algoritma C4.5

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