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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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“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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“Decision tree algorithms are used to classify the characteristics to be evaluated at any node to specify the “best” splitting [12].”
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“(4) Decision Tree (DT) Classifier: A DT [43] is a tree-like graph used as a decision support tool.”
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“More details about DT and RF algorithms’ operational procedures are covered in [51] and [52], respectively.”
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“It can be used for both continuous and discrete data sets (Patel & Prajapati, 2018).”
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“Classification and regression trees (CART): CART is a nonparametric supervised learning method [45, 46].”
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“The pathway from node to leaf represents the classification rule (Patel & Prajapati, 2018).”
An AIoT-based hydroponic system for crop recommendation and nutrient parameter monitorization
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“Regarding its performance, nonlinearity does not impact any of the DT parameters [38].”
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“A variable that has the highest gain will be a node in the decision tree (Patel and Prajapati, 2018).”
BackgroundFace mask detection in COVID-19: a strategic review
Vibhuti, Jindal, N., Singh, H., & Rana, P. S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12999-6 · Cited by 41
“In [73], decision trees and their specific algorithms are reviewed in depth.”
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“If the value is zero, then there is no relationship between the two variables, which suggests that their values will fluctuate depending on circumstances that are entirely arbitrary [11].”
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“48 Mean square error criterion is considered in splitting the data into trees for optimizing the model.”
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“Several DT techniques have been scrutinized, with CART emerging as the most precise and accurate in terms of accuracy, time, and precision [7].”
MethodHwang, J., Choi, K., Jeong, S., et al. (2024) · Current Research in Food Science · doi:10.1016/j.crfs.2024.100742 · Cited by 30
“A decision tree algorithm has a flowchart-like tree structure for both classification and regression problems (Patel and Prajapati, 2018).”
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“As decision trees mimic the human level thinking, it is simple to grab the data and make some good interpretations [80].”
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“(DT) [56], [57], Support vector machine (SVM) [58], [59],”
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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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“…was performed in all classification tasks, using the five below-listed techniques implemented with the StatExplore, HP Random Forest, Gradient Boosting, Variable Selection and HP Variable Selection nodes of SAS Enterprise Miner: (1) Decision or classification tree 13–15 with Chi-square split 16 .”
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Pear Classification using Machine Learning
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“The decision tree diagram is quite easy to understand, but determining the threshold value at each stage requires separate calculations based on information theory [25].”
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“Using Decision Tree, K-nearest Neighbors, and Logistic Regression, we achieve the highest accuracy on the Delta band of 57.30 percent, 60.89 percent, and 51.34 percent, respectively.”
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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].”
BackgroundInfluentialCross Deep Learning Method for Effectively Detecting the Propagation of IoT Botnet
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“A decision tree algorithm (DT) [48] supports the decisions and the potential outcome.”
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“The decision tree algorithm (Charbuty and Abdulazeez, 2021; Patel and Prajapati, 2018) was used to analyze users’ motion records and preferences, and predict future behavior and health needs.”
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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
Hajjej, F., Alohali, M. A., Badr, M., & Rahman, M. A. (2022) · BioMed Research International · doi:10.1155/2022/9449497 · Cited by 17
“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.”
BackgroundMethodRetractedAfify, H. M., Darwish, A., Mohammed, K. K., & Hassanien, A. E. (2020) · Ingénierie des systèmes d information · doi:10.18280/isi.250505 · Cited by 17
“In this paper, the selected features are applied to classification algorithms such as KNN [37] and decision tree [38].”
MethodHamayel, M. J., Abumohsen, M., & Moreb, M. (2021) · doi:10.1109/icit52682.2021.9491636 · Cited by 17
The Use of Prospect Theory for Energy Sustainable Industry 4.0
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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).”
Prediction of ECOG Performance Status of Lung Cancer Patients Using LIME-Based Machine Learning
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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
Analyzing drop coalescence in microfluidic devices with a deep learning generative model
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“40,41 To avoid overfitting, some parameters, such as max depth d max , should be set to enhance its robustness.”
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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
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
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
“[ 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
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
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
“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
Bali, A., & Mansotra, V. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17530-z · Cited by 12
“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
“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
“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
“…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
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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