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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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“So, here we will be examining the microarray datsets using traditional machine learning algorithms and models such as decision trees (DT) [7], random forest (RF) [8], logistic regression (LR) [9], k-nearest neighbor (KNN) [10], [11], and Support Vector Machine (SVM) [12]–[14].”
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“The overarching idea is to construct such a tree for the complete dataset, yielding a distinct outcome at each leaf [22].”
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“Building a tree is a multistage process where all predictors are analyzed at each stage and the one providing the most homogenous subsets is selected [43].”
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“…of the methodological apparatus, the decision tree has industrial applications both in different areas of economic science, which is ref lected, for example, in works and other areas of activity, in particular, in information systems and management of technical systems (Patel & Prajapati, 2018).”
MethodDDoS Intrusion Detection Model for IoT Networks using Backpropagation Neural Network
Almotiri, J. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130682 · Cited by 4
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“Decision trees are capable of solving problems, disregarding whether the input is discrete or continuous [16].”
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“It can also be used for classification as well as regression [22].”
MethodA Study on Network Intrusion Detection System
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“The LAN case used a decision tree [7] model and achieved these results with minimal CPU and RAM usage.”
MethodJaganathan, D., & Ayyanathan, N. (2022) · Algorithms for intelligent systems · doi:10.1007/978-981-16-6332-1_38 · Cited by 4
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“Decision Trees (DTs) are non-parametric supervised learning methods widely used for classification and regression tasks (Patel and Prajapati 2018; Sarasa-Cabezuelo 2022).”
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“It helps to divide data into training datasets when the sample dataset size is too large [11].”
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“It can solve in both situations whether one has discrete or continuous data as input [20].”
BackgroundClassified disease detection from MEMS based biosensor array
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“KNN[6], Support Vector Machine[7], Decision tree[8], and Random Forest[9] after testing on the cocoa bean test dataset.”
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Feature Selection using Genetic Algorithm for Microarray Data Classification
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“So we will do a feature selection using genetic algorithm [15] combined with the classification [16]–[18] algorithms such as Random Forest [19], decision tree [20], [21], and logistic regression [22].”
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“The reasons for choosing these three models are, AdaBoost is a model that provides good performance in classifying [21], decision tree gives high accuracy results for the classification process [22], while the random forest model gives good accuracy results in classifying [23].”
MethodData Mining for Managing and Using Online Information on Facebook
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“Fatima and Srinivasu [30] applied SVM algorithms for document classification.”
BackgroundMethodInfluentialINTELLIGENT HOME: EMPOWERING SMART HOME WITH MACHINE LEARNING FOR USER ACTION PREDICTION
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“As a typical data mining method, decision trees can not only analyze the characteristic trends of large-scale sports data, but also accurately predict the future development trends of sports teaching decision-making [17].”
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“Overall the performance of the DT method in the classification analysis process will provide maximum results [31].”
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“The Decision Tree classifier implements ML algorithm as a white box type model (Patel & Prajapati, 2018).”
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“In this tree-like structure, the internal nodes represent the features and attributes of the data, the branches represent the decision rules, and the leaf nodes represent the outcomes [34].”
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BackgroundFeature Selection using Ant Colony Optimization for Microarray Data Classification
Prajapati, S., Das, H., & Gourisaria, M. K. (2023) · International Conference on Information Systems and Computer Networks (ISCON) · doi:10.1109/iscon57294.2023.10112054 · Cited by 3
“This study utilizes the Ant Colony Optimization method to optimize feature selection, and it employs machine learning classification techniques such as Logistic Regression [24], Decision Tree (DT) [25], [26], and Random Forest (RF) [27].”
MethodUser Input Based Health Risk Assessment to Predict Diabetes, Obesity and Heart Risk factors
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“It is made up of leaf nodes, branches, and root nodes [33].”
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BackgroundImproving Salary Offer Processes With Classification Based Machine Learning Models
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“Information Gain, Gain Ratio and Gini Index metrics are used for decision tree algorithm feature selection [9].”
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“Comparing decision tree algorithms [12] Not specified Avg = 92.94 Sentiment analysis on mobile product reviews in Kannada [6] RF “GadgetLoka” RF = 72.0 The traditional machine learning approach consists of two primary methods for sentiment analysis, which are supervised learning and unsupervised…”
InfluentialREDUCE: a semi-supervised scalable approach for REsult DUplication detection in Search Engines
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“The next step is to use traditional machine learning methods for training, such as decision trees (DT) [29], logistic regression (LR) [30], K-nearest neighbors (KNN) [31], and deep neural networks (DNN).”
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“Decision tree is a fundamental machine learning algo-rithm [23], that based on training data gradually forms a tree by best splitting results according to the homogeneity of the produced splits.”
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“Teaching ranking management is displayed in the form of a list, and the comprehensive score value is calculated according to the weight of the evaluation subject [12].”
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“A decision tree is a concept in which data is replaced by a decision tree and decision rules [12]–[14].”
BackgroundEmpirical Analysis of Humor Detection Using Deep Learning and Machine Learning on Kaggle Corpus
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Self-citationGUI Based Machine Learning Algorithms to Predict Alzheimer’s Disease
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“A decision tree only poses a question and divides the tree further into subtrees based on the response (yes, no), (Patel and Prajapati, 2018).”
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“It is robust to outliers and able to handle nonlinear data relationships (Ahmim et al., 2019; Humbird et al., 2018; Patel et al., 2018).”
BackgroundA sentiment analysis of madura island tourism news using C4.5 algorithm
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“One classification technique in text mining is the Decision Tree (C4.5) [20].”
MethodSentiment Analysis of Visitor Reviews on Hotel In West Sumatera
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“In terms of performance, non-linear has no effect on any of the decision tree’s characteristics [43].”
BackgroundAutomated and Interpretable Fake News Detection with Explainable Artificial IntelligencePreprint
Giri, M., Honnavalli, P. B., & Eswaran, S. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4076594 · Cited by 2
Explainable AI Prediction of Cooking Oil Prices Over Time
Azzuri, F., Darfiansa, L. S., Grananta, R. S., Permatasari, A. I., & Yudistira, N. (2022) · International Conference on Sustainable Information Engineering and Technology (SIET) · doi:10.1145/3568231.3568240 · Cited by 1
“The decision tree generates all possible alternatives and traces each alternative to its conclusion in a clear, unambiguous, and straightforward manner so that easy comparisons can be made between alternatives [Patel and Prajapati 2018].”
BackgroundMBIST Area & Test Time Optimization Using Machine Learning
Jamal, D., & Veetil, R. T. (2023) · IEEE International Test Conference India (ITC India) · doi:10.1109/itcindia59034.2023.10235617 · Cited by 1
“Decision Tree regressor is one of the optimum model choices as it mimics human thinking ability and makes decisions based on given conditions [9].”
BackgroundMethodA Decision Tree of Bioengineering Study and Career Path for Educational Guidance
Areeprayolkij, W., & Li, M. (2023) · Proceedings - Asia Pacific Software Engineering Conference/Proceedings · doi:10.1109/apsec60848.2023.00066 · Cited by 1
Jalali, M., Niazkar, H. R., Johari, M. G., Saem, A., & Rezaianzadeh, A. (2024) · Epidemiology and Health System Journal · doi:10.34172/ehsj.26113 · Cited by 2
Fahrudin, N. F., Putra, K. R., Umaroh, S., & LAUTAN, G. B. (2024) · MIND Journal · doi:10.26760/mindjournal.v9i2.220-234 · Cited by 1
“As Figure 3 shows, feature scaling is not needed in this process because the nature of the decision tree itself is invariant, so it will not affect the accuracy value of the model itself (Patel & Prajapati, 2018) .”
Schler, J., Zubtsovsky, S., & Tuval‐Mashiach, R. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126559 · Cited by 1
Queipo, M., Mateo, J., Torres, A., & Barbado, J. (2025) · Biomedicines · doi:10.3390/biomedicines13040803 · Cited by 1
“…the comparative analysis were Gaussian Naive Bayes (GNB) [34,35], k-Nearest Neighbors (KNNs) [36,37], Bayesian Linear Discriminant Analysis (BLDA) [38,39], Support Vector Machines (SVM) [40], decision trees (DT) [41,42], and the novel system developed in this study, Random Forest (RF) [32,43,44].”
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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