Analysis of data mining classification by comparison of C4.5 and ID algorithms

Sudrajat, R.; Irianingsih, Iin; Krisnawan, D.

doi:10.1088/1757-899x/166/1/012031

Cited by 17 publications

(8 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the model must be able to extract relevant information from these in either a discrete or continuous problem (Doquire & Verleysen, ). Whether the data are discrete or continuous can also have an impact on the accuracy of the model (Sudrajat, Irianingsih, & Krisnawan, ). The Adaption (SC11) subcriterion was also an element identified within the flexibility criterion.…”

Section: Selecting the Best Classifier For The Adoption Of A Remindinmentioning

confidence: 99%

Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

Ortíz‐Barrios

Nugent

Cleland

et al. 2019

Multi Criteria Decision Anal

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The number of people with dementia (PwD) is increasing dramatically. PwD exhibit impairments of reasoning, memory, and thought that require some form of self‐management intervention to support the completion of everyday activities while maintaining a level of independence. To address this need, efforts have been directed to the development of assistive technology solutions, which may provide an opportunity to alleviate the burden faced by the PwD and their carers. Nevertheless, uptake of such solutions has been limited. It is therefore necessary to use classifiers to discriminate between adopters and nonadopters of these technologies in order to avoid cost overruns and potential negative effects on quality of life. As multiple classification algorithms have been developed, choosing the most suitable classifier has become a critical step in technology adoption. To select the most appropriate classifier, a set of criteria from various domains need to be taken into account by decision makers. In addition, it is crucial to define the most appropriate multicriteria decision‐making approach for the modelling of technology adoption. Considering the above‐mentioned aspects, this paper presents the integration of a five‐phase methodology based on the Fuzzy Analytic Hierarchy Process and the Technique for Order of Preference by Similarity to Ideal Solution to determine the most suitable classifier for supporting assistive technology adoption studies. Fuzzy Analytic Hierarchy Process is used to determine the relative weights of criteria and subcriteria under uncertainty and Technique for Order of Preference by Similarity to Ideal Solution is applied to rank the classifier alternatives. A case study considering a mobile‐based self‐management and reminding solution for PwD is described to validate the proposed approach. The results revealed that the best classifier was k‐nearest‐neighbour with a closeness coefficient of 0.804, and the most important criterion when selecting classifiers is scalability. The paper also discusses the strengths and weaknesses of each algorithm that should be addressed in future research.

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Section: Selecting the Best Classifier For The Adoption Of A Remindinmentioning

confidence: 99%

Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

Ortíz‐Barrios

Nugent

Cleland

et al. 2019

Multi Criteria Decision Anal

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“…C4.5 conducts the classification by calculating the information gain ratio of each attribute, and chooses attributes with the biggest information gain ratio as the root node. To calculate the information gain ratio precisely, entropy carried by a data set of possible distribution values V is first calculated using Equation (1), as follows [56]:…”

Section: Experiments Methodsmentioning

confidence: 99%

Machine Learning-Based Detection for Cyber Security Attacks on Connected and Autonomous Vehicles

Meng

et al. 2020

Mathematics

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Connected and Autonomous Vehicle (CAV)-related initiatives have become some of the fastest expanding in recent years, and have started to affect the daily lives of people. More and more companies and research organizations have announced their initiatives, and some have started CAV road trials. Governments around the world have also introduced policies to support and accelerate the deployments of CAVs. Along these, issues such as CAV cyber security have become predominant, forming an essential part of the complications of CAV deployment. There is, however, no universally agreed upon or recognized framework for CAV cyber security. In this paper, following the UK CAV cyber security principles, we propose a UML (Unified Modeling Language)-based CAV cyber security framework, and based on which we classify the potential vulnerabilities of CAV systems. With this framework, a new CAV communication cyber-attack data set (named CAV-KDD) is generated based on the widely tested benchmark data set KDD99. This data set focuses on the communication-based CAV cyber-attacks. Two classification models are developed, using two machine learning algorithms, namely Decision Tree and Naive Bayes, based on the CAV-KDD training data set. The accuracy, precision and runtime of these two models when identifying each type of communication-based attacks are compared and analysed. It is found that the Decision Tree model requires a shorter runtime, and is more appropriate for CAV communication attack detection.

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“…CodeIgniter was established and developed in accordance with the Model-View-Controller (MVC) paradigm (Endra, Cucus, & Wulandana S, 2020). Model-View-Controller (MVC) is a notion established by Smalltalk's founder (Trygve Reenskaug) to summarize data together with its processing (model), isolate it from the manipulation process (controller), and show it on the user interface (view) (Sudrajat, Irianingsih, & Krisnawan, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Web-Based New Student Admission Information System Using Waterfall Method

Aldi

2022

SinkrOn

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The current development of information technology on the activities of people's daily lives is very influential. Where in doing an activity that takes a lot of time can be shortened to complete it. One of the things that we can see at this time is in the field of education. Education service technology is feasible if the government, education service providers, and the community all work together. The problem is how to build information system technology that can provide educational service processes without intervening in existing operational standards. The New Student Admissions Information System is a website-based platform designed to enable public access to information about schools and the new student registration process. The waterfall approach was used to construct this information system, together with the PHP programming language, the Codeigniter 3 framework, the MVC pattern, and the MySQL database. The results obtained in the form of a new student admission information system website that provides services related to registration, and processing of registration data by the school administration. With this research, it is expected to facilitate new student admission officers at Al Azhar Islamic Elementary School 32 Padang to process data well, and help parents to register their children as new students at Al Azhar Islamic Elementary School 32 Padang.

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Analysis of data mining classification by comparison of C4.5 and ID algorithms

Cited by 17 publications

References 1 publication

Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

Machine Learning-Based Detection for Cyber Security Attacks on Connected and Autonomous Vehicles

Web-Based New Student Admission Information System Using Waterfall Method

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