Development of the Low Cost Classroom Response System Using Test-Driven Development Approach and Analysis of the Adaptive Capability of Students Using Sequential Minimal Optimization Algorithm

Ibarra, Joseph Bryan G.; Caya, Meo Vincent C.; Bentir, Sarah Alma P.; Paglinawan, Arnold C.; Monta, Jim Joseph; Penetrante, Francis C.; Mocon, Jumarie; Turingan, Jerry

doi:10.1109/iea.2019.8714889

Cited by 4 publications

(1 citation statement)

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“…Usually, it reduces training time by a wide margin. SMO uses “John Platt’s sequential minimal optimization algorithm” [ 35 ] for training an SVM [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

Breast Tumor Classification Using an Ensemble Machine Learning Method

2020

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Breast cancer is the most common cause of death for women worldwide. Thus, the ability of artificial intelligence systems to detect possible breast cancer is very important. In this paper, an ensemble classification mechanism is proposed based on a majority voting mechanism. First, the performance of different state-of-the-art machine learning classification algorithms were evaluated for the Wisconsin Breast Cancer Dataset (WBCD). The three best classifiers were then selected based on their F3 score. F3 score is used to emphasize the importance of false negatives (recall) in breast cancer classification. Then, these three classifiers, simple logistic regression learning, support vector machine learning with stochastic gradient descent optimization and multilayer perceptron network, are used for ensemble classification using a voting mechanism. We also evaluated the performance of hard and soft voting mechanism. For hard voting, majority-based voting mechanism was used and for soft voting we used average of probabilities, product of probabilities, maximum of probabilities and minimum of probabilities-based voting methods. The hard voting (majority-based voting) mechanism shows better performance with 99.42%, as compared to the state-of-the-art algorithm for WBCD.

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“…Usually, it reduces training time by a wide margin. SMO uses “John Platt’s sequential minimal optimization algorithm” [ 35 ] for training an SVM [ 36 ].…”

Section: Methodsmentioning

confidence: 99%