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1
Case Slicing Technique for Feature Selection
Published 2004“…This technique with k = 10 has been used in this thesis to evaluate the proposed approach. CST was compared to other selected classification methods based on feature subset selection such as Induction of Decision Tree Algorithm (ID3), Base Learning Algorithm K-Nearest Nighbour Algorithm (k-NN) and NaYve Bay~sA lgorithm (NB). …”
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2
Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd
Published 2022“…Classification is a data mining technique used to classify varied data types according to a specific criterion. …”
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3
Detection and classification of conflict flows in SDN using machine learning algorithms
Published 2021“…Using a range flows from 1000 to 100000 with an increment of 10000 flows per step in two network topologies namely, Fat Tree and Simple Tree Topologies, that were created using the Mininet simulator and connected to the Ryu controller, the performance of the proposed algorithms was evaluated for efficiency and effectiveness across a variety of evaluation metrics. …”
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4
Analysis and comparison of classification algorithms for credit approval in Islamic banks
Published 2025“…Although Islamic banks do not charge interest, profits are obtained through contracts, requiring thorough and accurate credit analysis. This study evaluates the performance of various classification algorithms, including C4.5, Random Forest, Decision Tree, K-Nearest Neighbors, and Naïve Bayes, in predicting credit approval decisions based on factors such as character, capacity, capital, collateral, and economic conditions. …”
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5
Evaluation of fall detection classification approaches
Published 2012“…The acceleration data with a total data of 6962 instances and 29 attributes were used to evaluate the performance of the different classification algorithm. …”
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6
Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram
Published 2021“…The parameters involved to estimate the mangrove age are differences feature selection and different supervised machine learning algorithm. The support vector machine (SVM) which is one of the machines learning algorithms for object-based image analysis (OBIA) method is used in this study for the classification of the mangrove from other LULC. …”
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7
Tree-based contrast subspace mining method
Published 2020“…The research works involve first preparing the real world numerical and categorical data sets. Then, the tree-based method, the genetic algorithm based parameter values identification of tree-based method, and followed by the genetic algorithm based tree-based method, for numerical data sets are developed and evaluated. …”
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8
Object-based imagery analysis for automatic urban tree species detection using high resolution satellite image
Published 2016“…This study also explores the use and comparison of object-based classification, and two common pixel-based classification methods namely, maximum likelihood and support vector machines based on WorldView-2 satellite imagery to evaluate the potential of the object-based in compare to pixel-based to detect urban tree species. …”
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9
Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree
Published 2021“…The proposed algorithm is used to fetch relevant features based from the high-dimensional input feature space. …”
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Lightning fault classification for transmission line using support vector machine
Published 2023“…Overall, SVM algorithm performed better than k-NN in terms of classification accuracy, achieving a value of 97.10% compared to k-NN’s 70.60%. …”
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11
A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem
Published 2016“…There are two methods in dealing with imbalanced classification problem, which are based on data or algorithmic level. …”
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12
Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals
Published 2020“…In this regard, this thesis proposes five FS methods for efficient EMG signals classification. The first method is the Binary Tree Growth Algorithm (BTGA), which implements a hyperbolic tangent function to convert the Tree Growth Algorithm into the binary version. …”
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13
Lightning Fault Classification for Transmission Line Using Support Vector Machine
Published 2024“…Overall, SVM algorithm performed better than k-NN in terms of classification accuracy, achieving a value of 97.10% compared to k-NN's 70.60%. …”
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14
An enhanced feature selection technique for classification of group based holy Quran verses
Published 2018“…The proposed FS technique is a combination of filter-based information gain (IG) and wrapper-based CFS algorithms. …”
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15
Modeling forest fires risk using spatial decision tree
Published 2011“…This paper presents our initial work in developing a spatial decision tree using the spatial ID3 algorithm and Spatial Join Index applied in the SCART (Spatial Classification and Regression Trees) algorithm. …”
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16
Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms
Published 2024“…Using metrics, this study evaluates Random Forest, ElasticNet, and Decision Tree algorithms. …”
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An enhanced feature selection technique for classification of group-based holy quran verses
Published 2018“…The proposed FS technique is a combination of JUter-based information gain (JG) and wrapper-based CFS algorithms. …”
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18
Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…Then, the classifier (support vector machine (SVM) and data mining (DM) algorithm, decision tree (DT) were applied on each fusion image and their accuracy were evaluated. …”
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19
A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization
Published 2018“…© 2018, Springer International Publishing AG.…”
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A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization
Published 2018“…© 2018, Springer International Publishing AG.…”
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