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Study and Analysis of Decision Tree Based Classification Algorithms
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International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
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“Olshausen & Field (1997) [186] introduced sparse coding for the first time as a machine learning technique for learning over-complete basis to create effective data representation reducing the dimensional of data and dynamically represent the data as a linear combination of basis vectors.”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [186]
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“Decision Tree (DT) Decision Tree (DT) [14, 15] is known as a machine learning classifier.”
BackgroundDefect structure classification of neutron-irradiated graphite using supervised machine learning
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“The equations in the MRL can be seen in Formulas 2 & 3 [27]: (2)”
BackgroundMorphological Image Processing for Cocoa Bean Classification
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Vasanth, R., & Pandian, A. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-0095-2_28 · Cited by 1
IMPLEMENTASI ALGORITMA DECISION TREE C4.5 DENGAN IMPROVISASI MEAN DAN MEDIAN PADA DATASET NUMERIK
Febiani, N., Fauzan, A. C., & Hudа, M. M. (2022) · Jurnal Teknik Informasi dan Komputer (Tekinkom) · doi:10.37600/tekinkom.v5i1.435 · Cited by 1
“Menurut Patel dan Prajapati [19], root node atau akar merupakan induk dari semua node dan merupakan node tertinggi pohon pada algoritma decision tree.”
BackgroundObot, O. U., Attai, K. F., & Onwodi, G. O. (2022) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-6684-5092-5.ch001 · Cited by 1
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“These events are then labeled and indexed [3] for later retrieval.”
BackgroundSelection of browsers for smartphones: a fuzzy hybrid approach and machine learning technique
Ramathilagam, A., Pitchipoo, P., Krishnasamy, V., Ramasamy, R., & Rajakarunakaran, S. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-022-01778-2 · Cited by 1
“Patel and Prajapati [25] have studied the decision-tree-based classification algorithms.”
BackgroundBusiness Intelligence System for Human Resource Management System
Fraihat, S., Innab, H., Makhadmeh, S. N., et al. (2022) · doi:10.1109/etcea57049.2022.10009861 · Cited by 1
“Table 3 illustrates the best accuracy score for each classifier model, such as Decision Tree [12], Random Forest [2], K-Nearest Neighbor [13], Support Vector Machine [7], Logistic Regression [7, 14], Bagging Classifier [15], AdaBoost Classifier [16].”
MethodEmotion Detection using Facial Image for Behavioral Analysis
R, U., Vasantha, S., G., S., & Md, A. (2022) · 2022 International Conference on Futuristic Technologies (INCOFT) · doi:10.1109/incoft55651.2022.10094501 · Cited by 4
“This project proposes to apply various machine learning algorithms such as Decision Tree classifier, Random Forest classifier and Deep learning algorithm ANN to identify state of the facial emotions inorder to further detect mental health in a targeted people [5][6][7].”
BackgroundMethodRetractedIOT Devices Using Supervised Machine Learning Models for Anomaly Based Intrusion Detection
Divakarla, U., & Chandrasekaran, K. (2023) · International Conference on Emerging Smart Computing and Informatics (ESCI) · doi:10.1109/esci56872.2023.10099676 · Cited by 1
“: 3) Decision Tree A decision tree is a type of tree structure that mimics a flowchart, where each internal node stands in for a test on an attribute, each branch for a test result, and each leaf node (terminal node) for a class label [15].”
BackgroundPanjaburee, P., Hwang, G., Intarakamhang, U., et al. (2023) · IEEE Eurasian Conference on Educational Innovation (ECEI) · doi:10.1109/ecei57668.2023.10105413 · Cited by 1
“The tree hierarchy also impersonates the human’s cognitive process making it easy to comprehend the data [22].”
BackgroundApplying Machine Learning to Luxury Marketing: Utilizing Prediction Model to Develop Brand Strategy
Jing, Q. (2023) · Highlights in Business Economics and Management · doi:10.54097/hbem.v10i.8115 · Cited by 1
AI-based Cavitation Detection in Process Valves
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“There are four machine learning methods used – support vector machine (SVM) [14], decision tree [15], tree bagger [16] using gentle boost to fit an ensemble of regression trees and K-nearest neighbours (KNN) [17].”
MethodParticle Swarm Optimization Untuk Optimasi Klasifikasi Tingkat Kepuasan Layanan Publik
Priatna, W. (2023) · Indonesian Journal of Computer Science · doi:10.33022/ijcs.v12i5.3441 · Cited by 1
Gene Family Classification Using Machine Learning: A Comparative Analysis
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Utilising Machine Learning to Predict Myocardial Infarction by Electrocardiogram Derived Respiration
Fung, E., & Ghiasi, S. (2023) · Journal of Student Research · doi:10.47611/jsrhs.v12i3.5041 · Cited by 1
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Development of a Model for Predicting Students’ Achievement
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Early and Rapid COVID-19 Diagnosis Using a Symptom-Based Machine Learning Model
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“In a decision tree, the nodes serve as features (attributes), while the branches represent the decision points that lead to the leaves, which produce outcomes [22], [23].”
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“When constructing a tree, data is recursively divided into smaller groups based on specific rules, such as comparing feature values [15].”
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“With a notable accuracy of 94% in CKD prediction, it shows its efficacy in both discrete and continuous input scenarios [18].”
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“Harsh and Purvi 41 applied the decision tree model on different fields demonstrating its ability to replace the statistical procedures through offer of superior accuracy compared to other models.”
Azzeri, A. B., Hakim, N. F. A., Jaafar, H., et al. (2020) · Journal of Health and Translational Medicine · doi:10.22452/jummec.sp2020no1.3 · Cited by 1
“In the second projections, a decision tree technique (8) was used to estimate a cumulative number of positive cases based on the present clusters of Covid-19 and also several events that could have caused another surge of infection.”
MethodClustering Results Interpretation of Continuous Variables Using Bayesian Inference
Balabaeva, K., & Kovalchuk, S. V. (2021) · Studies in health technology and informatics · doi:10.3233/shti210204 · Cited by 1
“The big amount of interpretation approaches can be classified as model-specific [8] and model agnostic methods [9].”
BackgroundA Comparative Study of Predicting Students’ Academic Performance Using Classification Algorithms
Silva, M. P. R. I. R., Rupasingha, R. A. H. M., & Kumara, B. T. G. S. (2022) · International Conference on Advanced Research in Computing (ICARC) · doi:10.1109/icarc54489.2022.9753729 · Cited by 1
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“Entropy is an information theory measure used to determine the characteristics of an existing dataset [14], [15].”
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“The proposed method has been analyzed in comparison with other well-known ML algorithms Naïve Bayes (NB) [13], Support Vector Machine (SVM) [14], Decision Tree (DT) [15], Multi-Layer Perceptron (MLP) [16], and K-”
MethodPredicting Faculty Research Productivity using J48 Decision Tree Algorithm
Canlas, R. B., Piad, K. C., Lagman, A. C., et al. (2021) · International Conference on Information Technology: IoT and Smart City (ICIT) · doi:10.1145/3512576.3512624
“It provides all possible alternatives and traces them according to each conclusion in a single view in order to make it easy for comparing among the various alternatives [16].”
BackgroundMachine Learning Empowered Urdu Characters Recognition Mechanism
(2021) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2021/1401032021
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Dr. Senthil Kumar, M., Mr. Sakthivel, S., Mr. Sasitharan, M., et al. (2022) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2022.43366
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Descubrimiento de nuevos anticonvulsivos que actúan mediante interacciones con canales iónicos
Llanos, M. A. (2022) · El Servicio de Difusión de la Creación Intelectual (National University of La Plata) · doi:10.35537/10915/140503
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“0, Chi-square automatic interaction detection, and MARS [28].”
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Jayadev, G., Gayathri, K. M., Babu, T., et al. (2022) · International Conference on Communication, Computing and Industry 4.0 (C2I4) · doi:10.1109/c2i456876.2022.10051493
“It uses supervised learning .[9][10] • Random Forest - This technique uses several decision trees to classify data.”
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(2023) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets35476
Rayleigh-quotient-based Data Classification Method for Machine LearningPreprint
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“Model regresi Decision Tree terdiri dari akar, cabang, dan daun yang dapat memecah dataset menjadi subset yang lebih kecil [11].”
BackgroundExploring Effective Approaches To Minimize Downtime In Final Assembly Line Of Braking Systems
Madhav, S., Math, M. M., Shivaraj B. W, et al. (2023) · Journal of Namibian Studies History Politics Culture · doi:10.59670/jns.v35i.4246
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Impact of Feature Extraction Combined with Data Sampling Methods on Heartbeat Categorization
Et al. Sampath Kini, K. (2024) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i9.9770
Integrating ML Algorithms in Graph Database for Link Prediction
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“The algorithm constructs a tree-like model by recursively dividing the data into subsets based on the attribute values until a halting criterion, such as a maximum depth or minimal number of samples in a leaf node, is met [16].”
MethodInterception amelioration of Chipless RFID Tags Using Deep Learning Techniques
Thomas, A. (2023) · Journal of Science and Technology · doi:10.46243/jst.2023.v8.i05.pp22-32
“A decision is reached using this non-parametric divide and conquers methodology by traversing a hierarchical graph from a root location to a leaf node (Patel & Prajapati, 2018).”
MethodQualitative Research Reasoning on Dementia Forecast Using Machine Learning Techniques
Kapdi, T., & Shah, A. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-9521-9_9
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Yoon, H., Kim, H., & Kim, S. (2024) · Measurement Interdisciplinary Research and Perspectives · doi:10.1080/15366367.2023.2246111
“Each quarter, the variable area is divided into two, and the learning proceeds in such a way that the purity of each area increases or the uncertainty (entropy) decreases (Patel & Prajapati, 2018).”
BackgroundSmart Farming: Developing a Crop Prediction Model for Productive AgriculturePreprint
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“Decision Tree bisa dianggap sebagai sebuah pohon di mana setiap simpul mewakili suatu fitur (atribut), setiap tautan (cabang) mewakili suatu keputusan (aturan), dan setiap daun mewakili hasil (nilai kategori atau nilai kontinu) [27].”
BackgroundA Review of Prediction on Alzheimer's Disease Using Machine Learning Techniques
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Twitter Sentiment Analysis for Polarity Detection: A Survey
., N., & Kumar, R. (2023) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n60023.2023.10541788
“Here, the entire dataset is supplied to the root node [18].”
Teneke, N. G., Etemi, J. G., Kamak, Y. S., et al. (2024) · ABUAD Journal of Engineering Research and Development (AJERD) · doi:10.53982/ajerd.2024.0701.33-j
Chattopadhyay, S., Roy, T., & Midya, M. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3423304 · Cited by 1
“The decision tree algorithm [34], [35] is used to construct the decision tree based on the values of the class attribute of the training dataset.”
MethodPoojashree, K., Meena, S., & Celsiya, C. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-5832-0.ch006
Xu, J. (2024) · Journal of Computational Methods in Sciences and Engineering · doi:10.3233/jcm-247556
Khumaidi, A., Darmawan, R., & Reztrianti, D. (2024) · Faktor Exacta · doi:10.30998/faktorexacta.v17i2.21819
“A widely used supervised learning method is the decision tree method, because it is easy to understand where the way of determining decisions in the tree method is similar to the way humans think [5].”
MethodDeciphering Fitness Application Data Using Machine Learning
Puniyani, S., Girotra, D., Agarwal, D., et al. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-4152-6_37
Personalized Guidance of Big Data Analysis in College English Listening Training
Liu, X. (2023) · doi:10.1109/ici3c60830.2023.00067
“Assuming that the training dataset is segmented based on a conditional attribute , the classification information entropy of conditional attribute for segmentation of relative to is represented as [9]: (2) Among them, is the number of values for condition attribute (cid:11) .”
MethodMachine Learning Algorithms for Emotion Detection using EEG Signals
Hossain, M. A., Biswas, S. K., Thounaojam, D. M., Khan, A., & Siddiquee, K. N. E. A. (2024) · IEEE International Conference on Computing, Applications and Systems (COMPAS) · doi:10.1109/compas60761.2024.10796614
“This method is recognized for its straightforward visual representation and its capability to manage both numerical and categorical data types [24].”
Rakheja, A., Roy, I., Pahwa, R., et al. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-7616-0_3
Admission Prediction for Universities Using Decision Tree Algorithm and Support Vector Machine
Trivedi, K., Dsouza, J., Kumar, S., et al. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-7356-5_17
Kumar, M. S. P., Khanna, R. R., Kumar, C. J., et al. (2024) · doi:10.1109/icicec62498.2024.10808947
“[4] Decision Tree is a supervised classification algorithm.”
Gunawan, M. T., Tine, J. Y., & Murwaningtyas, C. E. (2024) · Jurnal Pendidikan Informatika dan Sains · doi:10.31571/saintek.v13i2.7696
Decision Tree with Whale Optimization Algorithm (DT-WOA) for QoS-Based IoT Service Composition
Ouhadda, S. A. H., Sadouki, S. C., & Tari, A. (2024) · doi:10.1109/ecte-tech62477.2024.10851053
“A Decision Tree consists of nodes representing features (parameters), branches representing decisions, and leaves representing outcomes [22].”
Gadouh, K. A., & Sellami, H. K. (2024) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa63423.2024.10912551
“Advanced machine learning models including Random Forest, Decision Tree Regressor [17]- [18], Support Vector Regression and Linear Regression, are applied to this processed data.”
Optimizing Life Expectancy Rate Using Different Regression Models
Prajapati, R., Sharma, H. V., Gupta, R., & Dhaliwal, P. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-97-8602-2_10
Performance optimization of Learning Trees modules in the ECL repository
Shashank, B., Sudershan, K. S., Shobha, G., et al. (2024) · International Conference on Communication, Control and Intelligent Systems (CCIS) · doi:10.1109/ccis63231.2024.10932096
Andalib, M. (2025) · International Journal of Construction Education and Research · doi:10.1080/15578771.2025.2485061
“The Tree’s structure includes a root node, internal nodes, branches, and end nodes (Lamrini, 2021; Patel & Prajapati, 2018; Rokach & Maimon, 2005).”
Influential
Metadata
- Authors
- Patel, H. H., & Prajapati, P.
- ORCID
- Prajapati, Purvi
- Venue
- International Journal of Computer Sciences and Engineering
- Publisher
- ISROSET: International Scientific Research Organization for Science, Engineering and Technology
- Published
- 2018-10-31
- Volume
- 6
- Issue
- 10
- Pages
- 74-78
- ISSN
- 2347-2693 (ISSN-L)
- OpenAlex
- W2908445743
- Cited by
- 441
- References
- 0
- Citations by year
- 2026: 13 · 2025: 63 · 2024: 95 · 2023: 105 · 2022: 100 · 2021: 55 · 2020: 10
- FWCI
- 26.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Computer Sciences and Engineering (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- decision tree, machine learning classifiers, decision tree classification
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