Search Results - (( data classification modeling algorithm ) OR ( using solution using algorithm ))
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1
A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules
Published 2013“…Benchmark data sets from various fields were used to test the proposed algorithms. …”
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Talent classification using support vector machine technique / Hamidah Jantan, Norazmah Mat Yusof and Mohd Hanapi Abdul Latif
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Research Reports -
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A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification
Published 2023“…The performance of the proposed SCSO algorithm was compared with six state-of-the-art and recent wrapper-based optimization algorithms using the validation metrics of classification accuracy, optimum feature size, and computational cost in seconds. …”
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4
Support directional shifting vector: A direction based machine learning classifier
Published 2021“…The positional error of the linear function has been modelled as a loss function which is iteratively optimized using the gradient descent algorithm. …”
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Ideal combination feature selection model for classification problem based on bio-inspired approach
Published 2020“…Such a finding indicates that the exploitation of bio-inspired algorithms with ideal combination of wrapper/filtered method can contribute in finding the optimal features to be used in data mining model construction.…”
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Classification models for higher learning scholarship award decisions
Published 2018“…In this study, a data mining approach was used to propose a classification model of scholarship award result determination. …”
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Improved cuckoo search based neural network learning algorithms for data classification
Published 2014“…Specifically, 6 benchmark classification datasets are used for training the hybrid Artificial Neural Network algorithms. …”
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8
An enhanced soft set data reduction using decision partition order technique
Published 2017“…Also, the accuracy of original soft-set optimal and sub-optimal results have been improved using an intelligent SSR-BPSO-BBO algorithm. The computational cost of search domain (space) has been enhanced using proposed Markov Chain Model.…”
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9
Feature and Instances Selection for Nearest Neighbor Classification via Cooperative PSO
Published 2014“…This paper proposes the integration of feature reduction and data reduction for fuzzy modeling using Cooperative Binary Particle Swarm Optimization (CBPSO). …”
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Accuracy assessment of Digital Terrain Model (DTM) Constructed Cloth Simulation Filter (CSF) and Multi Curvature Classification (MCC) algorithm on UAV LiDAR dataset / Mohamad Khair...
Published 2023“…Two algorithms, the Cloth Simulation Filter (CSF) in CloudCompare and the Multiscale Curvature Classification (MCC) in Global Mapper, were tested for this purpose. …”
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Sentiment analysis for malay newspaper (SAMNews) using negative selection algorithm / Nur Amalina Redzuan
Published 2013“…In future, a comparative study on Artificial Immune System and other techniques or algorithms can be carried out to enhance the performance of the classification model.…”
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12
Diabetes Diagnosis And Level Of Care Fuzzy Rule-Based Model Utilizing Supervised Machine Learning For Classification And Prediction
Published 2024“…Therefore, a fuzzy model based on machine learning and data mining is a vital solution. …”
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Multi-class classification automated machine learning for predicting earthquakes using global geomagnetic field data
Published 2025“…It is concluded that the neural network multi-class classification model is capable of providing solutions to the challenges faced when using geomagnetic data for EQ prediction.…”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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15
A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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Optimizing in-car-abandoned children’s sounds detection using deep learning algorithms / Nur Atiqah Izzati Md Fisol
Published 2023“…To address this problem, an optimized in-car-abandoned children's sounds detection model using deep learning algorithms is proposed. …”
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Hybrid performance measures and mixed evaluation method for data classification problems
Published 2012“…This study investigates two different issues of performance measure in data classification problem. First, this study examines the use of accuracy measure as a discriminator for building an optimized Prototype Selection (PS) algorithm. …”
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18
Improving accuracy metric with precision and recall metrics for optimizing stochastic classifier
Published 2011“…Typically, all of stochastic classification algorithms employ accuracy metric to discriminate an optimal solution. …”
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Improving accuracy metric with precision and recall metrics for optimizing stochastic classifier
Published 2011“…All stochastic classifiers attempt to improve their classification performance by constructing an optimized classifier.Typically, all of stochastic classification algorithms employ accuracy metric to discriminate an optimal solution.However, the use of accuracy metric could lead the solution towards the sub-optimal solution due less discriminating power.Moreover, the accuracy metric also unable to perform optimally when dealing with imbalanced class distribution. …”
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20
K-gen phishguard: an ensemble approach for phishing detection with k-means and genetic algorithm
Published 2025“…In the second phase, the best set of features in each group is identified through the Genetic algorithm to enhance the classification process. Finally, a voting ensemble technique is applied, in which the Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Adaptive boosting (AdaBoost) models are combined. …”
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