Search Results - (( _ evaluation metric algorithm ) OR ( data classification using algorithm ))*

Evaluation of the Transfer Learning Models in Wafer Defects Classification by Jessnor Arif, Mat Jizat, Anwar, P. P. Abdul Majeed, Ahmad Fakhri, Ab. Nasir, Zahari, Taha, Yuen, Edmund, Lim, Shi Xuen

“…Three defects categories and one non-defect were chosen for this evaluation. The key metrics for the evaluation are classification accuracy, classification precision and classification recall. 855 images were used to train and test the algorithms. …”
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Improving accuracy metric with precision and recall metrics for optimizing stochastic classifier by Hossin, Mohammad, Sulaiman, Md. Nasir, Mustapha, Norwati, O. K. Rahmat, Rahmita Wirza

“…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 by M., Hossin, M.N., Sulaiman, N., Mustpaha, R.W., Rahmat

“…This paper demonstrates that the OARP metric is more discriminating than the accuracy metric and able to perform optimally when dealing with imbalanced class distribution using one simple counter-example.We also demonstrate empirically that a naïve stochastic classification algorithm, which is Monte Carlo Sampling (MCS) algorithm trained with the OARP metric, is able to obtain better predictive results than the one trained with the accuracy and FMeasure metrics.Additionally, the t-test analysis also shows a clear advantage of the MCS model trained with the OARP metric over the two selected metrics for almost five medical data sets.…”
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Case Slicing Technique for Feature Selection by A. Shiba, Omar A.

“…Since the 1960s, many algorithms for data classification have been proposed. …”
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Validation on an enhanced dendrite cell algorithm using statistical analysis by Mohamad Mohsin, Mohamad Farhan, Hamdan, Abdul Razak, Abu Bakar, Azuraliza, Abd Wahab, Mohd Helmy

“…This study demonstrates the mechanism of evaluating an enhanced algorithm using performance metrics and validated it using statistical analysis. …”
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Improving Accuracy Metric with Precision and Recall Metrics for Optimizing Stochastic Classifier by Hossin, M., Sulaiman, M.N, Mustapha, N., Rahmat, R.W

“…Typically, all of stochastic classification algorithms employ accuracy metric to discriminate an optimal solution. …”
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Classification models for higher learning scholarship award decisions by Wirawati Dewi Ahmad, Azuraliza Abu Bakar

“…Each model was evaluated using technical evaluation metric, such contingency table metrics, and accuracy, precision, and recall measures. …”
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Comparative study of machine learning algorithms in data classification by Tan, Kai Jun

“…The performance of these classifiers will be assessed using key evaluation metrics such as accuracy, precision, recall, f1-score, confusion matrix, AUC-ROC, and precision-recall. …”
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Loan Eligibility Classification Using Machine Learning Approach by Law, Paul Lik Pao

“…The models were then evaluated on the testing set using evaluation metrics such as Accuracy, Precision, Recall, And F1-Score. …”
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OAERP: a better measure than accuracy in discriminating a better solution for stochastic classification training by Hossin, Mohammad, Sulaiman, Md. Nasir, Mustapha, Aida, Mustapha, Norwati, O. K. Rahmat, Rahmita Wirza

“…This paper also empirically demonstrates that Monte Carlo Sampling (MCS) algorithm that is trained by OAERP metric was able to obtain better predictive results than the one trained by the accuracy metric alone, using nine medical data sets. …”
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Modified word representation vector based scalar weight for contextual text classification by Abbas Saliimi, Lokman

“…To bridge this gap, a five-phase research methodology is structured to propose and evaluate an algorithm enabling the external modification of LLM-generated word vectors using scalar values as the focus weightage. …”
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OAERP : A Better Measure than Accuracy in Discriminating a Better Solution for Stochastic Classification Training by Hossin, M., Sulaiman, M.N, Mustapha, A., Rahmat , R.W

“…This paper also empirically demonstrates that Monte Carlo Sampling (MCS) algorithm that is trained by OAERP metric was able to obtain better predictive results than the one trained by the accuracy metric alone, using nine medical data sets. …”
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Algorithm comparison for data mining classification: assessing bank customer credit scoring default risk by Elaf Adel Abbas, Nisreen Abbas Hussein

“…The models’ Accuracy, precision, recall, receiver operating characteristic (ROC) curve, and precision-recall curve were evaluated. Random Forest’s 97% ROC metric rating outperformed all other accuracy metrics. …”
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Analysis and comparison of classification algorithms for credit approval in Islamic banks by Pebrianti, Dwi, Wijanarko, Whena, Bayuaji, Luhur, Toha, Siti Fauziah

“…Using a dataset from an Islamic bank, the algorithms were assessed through precision, sensitivity, and accuracy metrics. …”
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Multi-label risk diabetes complication prediction model using deep neural network with multi-channel weighted dropout by Dzakiyullah, Nur Rachman

“…The study employed two MLC frameworks: Problem Transformation methods (Binary Relevance, Classifier Chains, Label Power Set, and Calibrated Label Ranking) and Algorithm Adaptation. Performance was evaluated using 20 metrics, including AUROC and other advanced indicators. …”
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Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms by Alswaitti, Mohammed Y. T.

“…Based on the obtained experimental results, the OGC, DPSO, and VDEO frameworks achieved an average enhancement up to 24.36%, 9.38%, and 11.98% of classification accuracy, respectively. All the frameworks also achieved the first rank by the Friedman aligned-ranks (FA) test in all evaluation metrics. …”
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A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification by Talpur, N., Abdulkadir, S.J., Hasan, M.H., Alhussian, H., Alwadain, A.

“…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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Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review by Goh, Choon-Hian, Ferdowsi, Mahbuba, Gan, Ming Hong, Kwan, Ban-How, Lim, Wei Yin, Tee, Yee Kai, Rosli, Roshaslina, Tan, Maw Pin *

“…The aims of the study were to systematically evaluate available machine learning (ML) algorithm for supporting syncope diagnosis to determine their performance compared to existing point scoring protocols. …”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

“…The method was implemented using Java and the results of the simulation were evaluated using five standard performance metrics: accuracy, AUC, precision, recall, and f-Measure. …”
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A Hybrid Evaluation Metric for Optimizing Classifier by Hossin, M., Sulaiman, M.N, Mustapha, A., Mustapha, N., Rahmat, R.W

“…In this study, we propose a hybrid evaluation metric, which combines the accuracy metric with the precision and recall metrics. …”
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