Predictive Hybridization Model integrating Modified Genetic Algorithm (MGA) and C4.5

Bustillo, James Cloyd M.; Medina, Ruji P.; Sison, Ariel M.; Orong, Markdy Y.

doi:10.1109/iceca55336.2022.10009532

Cited by 8 publications

(1 citation statement)

References 46 publications

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“…A. Barua et al (2021) used the most common machine learning methods, namely: Logistic Regression, Support Vector Classifier, Decision Tree(C4.5) [24], Naïve Bayes, Random Forest, and K-Nearest Neighbor, to classify articles about sports into four (4) different categories: Cricket, Football, Tennis, and Athletics. 80% of the data were used for training, while the other 20% were for testing [21].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Automated Categorization of Research Papers with MONO Supervised Term Weighting in RECApp

Biol¹,

Depositario²,

Noangay³

et al. 2023

IJACSA

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Natural Language Processing, specifically text classification or text categorization, has become a trend in computer science. Commonly, text classification is used to categorize large amounts of data to allocate less time to retrieve information. Students, as well as research advisers and panelists, take extra effort and time in classifying research documents. To solve this problem, the researchers used state-of-the-art supervised term weighting schemes, namely: TF-MONO and SQRTF-MONO and its application to machine learning algorithms: K-Nearest Neighbor, Linear Support Vector, Naive Bayes Classifiers, creating a total of six classifier models to ascertain which of them performs optimally in classifying research documents while utilizing Optical Character Recognition for text extraction. The results showed that among all classification models trained, SQRTF-MONO and Linear SVC outperformed all other models with an F1 score of 0.94 both in the abstract and the background of the study datasets. In conclusion, the developed classification model and application prototype can be a tool to help researchers, advisers, and panelists to lessen the time spent in classifying research documents.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%