Search Results - (( data classification (problems OR problem) algorithm ) OR ( data validation using algorithm ))
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1
Classification of credit card holder behavior using K Nearest Neighbor algorithm / Ahmad Faris Rahimi
Published 2017“…Testing of the application are using 10-Fold Cross Validation technique. 90% of the data be used as a training and another 10% be used for the testing part. …”
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Application of Optimization Methods for Solving Clustering and Classification Problems
Published 2011“…First we briefly give some data analysis background. Then a review of different methods currently available that can be used to solve clustering and classification problems is also given. …”
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3
Comparison Performance of Qualitative Bankruptcy Classification based on Data Mining Algorithms
Published 2018“…Knowledge discovery using data mining techniques are commonly applied in bankruptcy classification and prediction. …”
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Case Slicing Technique for Feature Selection
Published 2004“…Since the 1960s, many algorithms for data classification have been proposed. …”
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5
Hybrid ant colony optimization and genetic algorithm for rule induction
Published 2020“…The AntMiner classifier is efficient, useful and commonly used for solving rulebased classification problems in data mining. …”
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Computational Technique for an Efficient Classification of Protein Sequences With Distance-Based Sequence Encoding Algorithm
Published 2017“…The performance of the proposed technique is validated using some of the best performing classifiers implemented previously for protein sequence classification. …”
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7
Intent-IQ: customer’s reviews intent recognition using random forest algorithm
Published 2025“…Two machine learning model is chosen to build the classification models which are Random Forest (RF) algorithm and Multinomial Naïve Bayes (MNB) algorithm. …”
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Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak
Published 2019“…The fourth stage is to design evaluation methodology of Max-Min Rule-Based Classification Algorithm using classifier model. Finally, both algorithms are validated against the findings in various literatures. …”
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9
Integrated ACOR/IACOMV-R-SVM Algorithm
Published 2017“…Three benchmark UCI datasets were used in the experiments to validate the performance of the proposed algorithms. …”
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Effective gene selection techniques for classification of gene expression data
Published 2005“…Support Vector Machine (SVM) and k-nearest neighbour (knn) are used for the classification purposes. The proposed approach is validated using leave one out cross validation (LOOCV). …”
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An Arabic hadith text classification model using convolutional neural network and support vector machine / Mohd Irwan Mazlin
Published 2022“…There are 6 different ways of designing the experiment to evaluate the result of the study, which are an experiment with the model using different stemming techniques, an experiment with the model using three different algorithms, the result analysis of confusion matric of three algorithms, experiment the model using different SVM kernel, experiment the model using unseen data, produce precision, recall, F1-measure and accuracy result of the model and parameter. …”
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13
An improved method using fuzzy system based on hybrid boahs for phishing attack detection
Published 2022“…The experiment was executed by using k-fold cross validation techniques for predicting the classification algorithm performance. …”
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14
A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification
Published 2015“…Such appealing feature of the proposed algorithm has strictly fulfilled the criteria of being truly sequential, while many of the existing algorithms are not. …”
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Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi
Published 2019“…Based on the above components and circumstances, many studies have been performed on data clustering problems. Despite attempts to solve the data clustering issues, there are also many variants of modified algorithms in traditional information clustering that attempt to solve issues such as clustering algorithms based on condensation. …”
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SVM for network anomaly detection using ACO feature subset
Published 2016“…The selected feature subset using ant system is then validated using support vector machine. …”
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Green building valuation based on machine learning algorithms / Thuraiya Mohd ... [et al.]
Published 2021“…This paper provides an empirical study report, that building price predictions are based on green building and other general determinants. This experiment used five common machine learning algorithms namely 1) Linear Regressor, 2) Decision Tree Regressor, 3) Random Forest Regressor, 4) Ridge Regressor and 5) Lasso Regressor tested on a real estate data-set of covering Kuala Lumpur District, Malaysia. 3 set of experiments was conducted based on the different feature selections and purposes The results show that the implementation of 16 variables based on Experiment 2 has given a promising effect on the model compare the other experiment, and the Random Forest Regressor by using the Split approach for training and validating data-set outperformed other algorithms compared to Cross-Validation approach. …”
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Enhancement of new smooth support vector machines for classification problems
Published 2011“…Research on Smooth Support Vector Machine (SSVM) for classification problem is an active field in data mining. …”
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Modern fuzzy min max neural networks for pattern classification
Published 2019“…To build an efficient classifier model, researchers have introduced hybrid models that combine both fuzzy logic and artificial neural networks. Among these algorithms, Fuzzy Min Max (FMM) neural network algorithm has been proven to be one of the premier neural networks for undertaking the pattern classification problems. …”
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20
A Modified Hopfield Neural Network Algorithm (MHNNA) Using ALOS Image for Water Quality Mapping
Published 2016“…The TSS concentration measurements were conducted in a lab and used for validation (real data), classification, and accuracy assessments. …”
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