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) introduced sparse coding for the first time as a machine learning technique for learning over-complete basis to create effective data representation reducing the dimensionality of data and dynamically represent the data as a linear combination of basis vectors.
“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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“For Decision tree data came as records [186], [219], written as : The independent variables, represented by a vector x, consist of input variables such as X1, X2, X3, L, Xn, etc.”
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“We selected those supervised machine learning algorithms for classification because of their excellent performance and quick execution [29].”
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“The proportion of observations to total observations is known as the Information Gain Ratio (IGR) [44].”
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“A detailed description of a decision tree and its working can be found in [33].”
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“Information gain is used as a node impurity measure to select the attribute and split each node until reaching the last node, the so-called “leaf” [30].”
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“A decision tree is a tree-based approach in which data splitting decides every path from the root toward the leaf node until a Boolean result is attained at the leaf node [13].”
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[Retracted] A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data
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“Citing another work, we have the one by Hassani and Emami [9] dedicated to the theme of “ intelligent system for prognosis of survival of kidney transplant patient ” based on obtaining a system based on the hybrid knowledge for the prediction of time of survival renal graft survival of patients.”
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“In this paper, the selected features are applied to classification algorithms such as KNN [37] and decision tree [38].”
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“The model enables one to analyze many decisional variants (choice alternatives) and their assessment criteria at the same time [94].”
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“The tree structure consists of root nodes, internal nodes, and leaves, which partition the data based on various features (Patel and Prajapati, 2018).”
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“In this research, several machine learning algorithms are evaluated, namely Support Vector Machine (SVM)[16], K-Nearest Neighbour (KNN)[12], Naïve Bayes[17], Random Forest [18], and Decision Tree[19].”
MethodRachidi, Z., Khalid, C., & Kobbane, A. (2023) · International Conference on Wireless Networks and Mobile Communications (WINCOM) · doi:10.1109/wincom59760.2023.10322983 · Cited by 16 · see citing works
Analyzing drop coalescence in microfluidic devices with a deep learning generative model
Zhu, K., Cheng, S. L., Kovalchuk, N. M., et al. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp05975d · Cited by 14 · see citing works
“40,41 To avoid overfitting, some parameters, such as max depth d max , should be set to enhance its robustness.”
BackgroundVaferi, B., Dehbashi, M., Alibak, A. H., et al. (2024) · Marine and Petroleum Geology · doi:10.1016/j.marpetgeo.2024.106742 · Cited by 14 · see citing works
Ensemble Learning Techniques and Their Applications: An Overview
Dasari, A. K., Biswas, S. K., Thounaojam, D. M., et al. (2023) · Cognitive science and technology · doi:10.1007/978-981-19-8086-2_85 · Cited by 13 · see citing works
Alsumaidaie, M. S. I., Alheeti, K. M. A., & Alaloosy, A. K. (2023) · Iraqi Journal for Computer Science and Mathematics · doi:10.52866/ijcsm.2023.02.03.002 · Cited by 13 · see citing works
SQL Injection Detection using Machine Learning and Convolutional Neural Networks
Misquitta, J., & Asha, S. (2023) · International Conference on Smart Systems and Inventive Technology (ICSSIT) · doi:10.1109/icssit55814.2023.10061019 · Cited by 13 · see citing works
“[ 12] Decision Tree was chosen as the amount of pre-processing required here is very little.”
MethodA Data Mining Solution to Predict Campus Placement
Jain, P., Khare, S., & Gourisaria, M. K. (2021) · IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT) · doi:10.1109/gucon50781.2021.9573551 · Cited by 13 · see citing works
Optimized 1D-CNN model for medicinal Psyllium Husk crop mapping with temporal optical satellite data
Sabir, A., & Kumar, A. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101772 · Cited by 13 · see citing works
Prathyakshini, Preethi Salian, K., R, P. B., et al. (2022) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca55336.2022.10009447 · Cited by 12 · see citing works
“One of the non-parametric supervised learning algorithms is Decision tree [23] which is utilised for both classification and regression tasks.”
MethodEmpirical Analysis of Effect of Higher Order Harmonics on Guitar Chord Classification
Sharma, N., & Sharma, S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-9638-2_56 · Cited by 12 · see citing works
Bali, A., & Mansotra, V. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17530-z · Cited by 12 · see citing works
“The amount of entropy lost as a result of each split will be maximised because information gain is the most frequently utilised criterion [37, 38].”
Fariz, T. R., Permana, P. I., Daeni, F., et al. (2021) · Jurnal Geografi : Media Informasi Pengembangan dan Profesi Kegeografian · doi:10.15294/jg.v18i2.30231 · Cited by 12 · see citing works
“Decision tree ini mirip dengan cara manusia menentukan suatu keputusan, dimana ada proses pemilahan dalam penentuan informasi (Patel & Prajapati, 2018).”
BackgroundMachine Learning Approach to Predict Asthma Prevalence with Decision Trees
Mahammad, A. B., & Kumar, R. (2022) · International Conference on Technological Advancements in Computational Sciences (ICTACS) · doi:10.1109/ictacs56270.2022.9988210 · Cited by 12 · see citing works
“The performance of the CART algorithm depends on selecting appropriate splitting criteria; hence, the techniques play a vital role in the DT algorithm success rate [7, 17].”
MethodChoudhary, R., Walhekar, V. S., Muthal, A., et al. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2175263 · Cited by 12 · see citing works
“…2021) pi ¼ Probability of randomly selecting an example in class I Gini impurity: It demonstrate that how effectively a decision tree is divided and mathematically expressed as - where X ð j Þ is the proportion of records in class j and m is the total number of classes (Patel & Prajapati, 2018).”
Background
- 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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