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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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“Each attribute at each node will be tested to determine other tree branches (Patel & Prajapati, 2018).”
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“Internal nodes indicate databases, branches provide decision rules, and each leaf node delivers a conclusion to this tree-shaped divider [21].”
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“Each node shows a feature (attribute), each link (branch) shows a decision (rule), and each leaf shows a result (categorical or continuous value) [8].”
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“Each node represents a feature (attribute), each link creates a branch and leads to a decision (rule) and finally leads to an outcome (leaf node) [12] (p.”
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“2) Decision Tree [28]: Decision tree is a Supervised machine learning algorithm which can be used to solve both classification and regression problems.”
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“A decision tree (Patel and Prajapati, 2018) is a tree structure (can be binary or non-binary).”
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“It is well documented that males are more likely to be diagnosed with both ADHD [27] and ASD [28] than females.”
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“Decision trees, a data mining approach with a flowchart-like structure, effectively classify variables into a visual model, mirroring human cognitive processes (Patel & Prajapati, 2018).”
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“This algorithm consists of a node that represents a decision and a leaf that represents a solution [9].”
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“This study uses four machine learning algorithms as base learners, namely a decision tree with the Gini index criteria [18], a gradient-boosting decision tree [19], k-nearest neighbours [20], and a light gradient boosting machine [21].”
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“A detailed description of a decision tree and the workings of the decision can be found in [31,32].”
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“Data mining models such as linear regression [11], decision tree [12], support vector machine [13], deep neural network [14], and Bayesian network [15] are used in the literature [16].”
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“the decision Tree works by testing the attributes that are on the internal node, the test results will be on the branch and the results will be on the leaf node [17].”
BackgroundA Comparison of Surrogate Behavioral Models for Power Amplifier Linearization under High Sparse Data
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“These algorithms are able to operate on high-dimensional datasets and select the most biased feature, their comprehensibility nature makes it easier to interpret the model with an enhanced view of performance outcomes [27].”
BackgroundLoad forecast anomaly detection under cyber attacks using a novel approach
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“Base learners implemented in this paper are Decision Tree classification[8], Gradient Boosting classifier[9], Random forest classifier[10], K-Nearest Neighbour[11], Gaussian Naive Bayes[12], and Support Vector Classifier[13] which have good performance for multi-class classification.”
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“Another advantage of using decision tree is it is provided most biased feature [9].”
BackgroundA Novel Machine Learning Approach for Flood Prediction with Local Interpretable Explanations
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“Depending on the result of that test case, the prediction undergoes successive test cases and eventually reaches a terminal node providing the classification [10].”
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“𝑖𝑔𝑟(𝑡𝑥, 𝑓) = 𝑖𝑔/𝑖𝑣 Most of the researchers have used a decision tree machine learning technique in classifying the disease [65].”
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“Because of their consistency and adaptability, decision trees are frequently employed in categorization [8–12].”
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“Decision tree (DT) [44, 45]: An intuitive graphical method for classification by probability analysis, which does not require training of parameters and usually works well.”
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“Similar to a natural tree, the Decision Tree utilizes these elements to represent attributes, rules, and results [18].”
BackgroundMaulida, V., Herteno, R., Kartini, D., et al. (2023) · Journal of Electronics Electromedical Engineering and Medical Informatics · doi:10.35882/jeeemi.v5i4.315 · Cited by 5
“As with an ordinary tree, the Decision Tree comprises a root, branches, and leaves, adhering to the same structure[22].”
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“The decision tree is a versatile machine learning algorithm employed for tasks involving both classification and regression [8].”
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“This work contributes valuable knowledge to the field of machine learning, aiding researchers and practitioners in making informed choices when employing decision tree-based algorithms for classification purposes [6].”
BackgroundMethodThakur, K., Kumar, H., & Snehmani (2024) · Environmental Earth Sciences · doi:10.1007/s12665-024-11882-x · Cited by 5
“(Patel & Prajapati 2018).”
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“The Decision Tree (DT) structure also includes root nodes, branches, and leaf nodes, much like an actual tree [13].”
BackgroundUsing Slightly Imbalanced Binary Classification to Predict the Efficiency of Winter Road Maintenance
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“A DT consists of a root node, branches and leaf nodes [27].”
Applying Decision Tree Algorithm for Air Quality Prediction in Bangladesh
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“GRU (Gated Recurrent Units [16]) are a special type of Recurrent Neural Networks (RNNs).”
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Mahmoud, S., Gaber, M. M., Elhady, G. F., & Keshk, A. E.-S. (2023) · International Conference on Electronic Engineering (ICEEM) · doi:10.1109/iceem58740.2023.10319589 · Cited by 6
“In [14], the authors utilized SVM [7, 8], Artificial Neural Network (ANN) [9], NB [10], Decision Tree (DT) [15], LR [11], and KNN [13] to implement the heart dataset from the UCI repository.”
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“Hence we initially trained a Random Forest classifier [6] and a Decision Tree classifier [5].”
MethodApalkova, V., Tsyganov, S., Meshko, N., et al. (2022) · Problems and Perspectives in Management · doi:10.21511/ppm.20(2).2022.12 · Cited by 5
“…methodological apparatus, the decision tree has industry applications both in different areas of economic science (which is reflected, for example, in the works, and in other areas of activity, in particular, in information systems and the management of technical systems (Patel & Prajapati, 2018).”
BackgroundPholo, M. D., Hamam, Y., Khalaf, A., & Du, C. (2022) · Revue d intelligence artificielle · doi:10.18280/ria.360216 · Cited by 5
“One of the main advantages of decision trees is that they resemble human decision-making and are therefore easy to interpret [34, 39].”
Classification of Breast Cancer Diagnosis Using Machine Learning Algorithms
Dwivedi, R., Ramdev, K., Jain, E. M., et al. (2023) · International Conference on Computer Communication and Informatics (ICCCI) · doi:10.1109/iccci56745.2023.10128478 · Cited by 5
“[7] Figure 6.”
Stacked ensemble modeling for improved tuberculosis treatment outcome prediction in pediatric cases
Yılmaz, Y. (2024) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.8089 · Cited by 5
A decision tree-based classification of fetal health using cardiotocograms
Chuatak, J. V. Y., Comentan, E. R. C., Moreno, R. L. H. G., et al. (2023) · AIP conference proceedings · doi:10.1063/5.0111194 · Cited by 4
Design and Development of a Chatbot for Personalized Learning in Higher Education
Algabri, H. K., Kamat, R. K., Kharade, K. G., & Muppalaneni, N. B. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-6690-5_22 · Cited by 7
Background of machine learning
Qi, C., Yilmaz, E., & Chen, Q. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15524-6.00015-7 · Cited by 6
Monitoring River Ice in the Northern Section of Yellow River Using Sentinel-1/2 Images
Zhang, X., Xia, H., & Sheng, Y. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3408188 · Cited by 4
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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