Assessing the Predictive Performance of Machine Learning Algorithms: DBNs, Fuzzy ARTMAP, and SVMs

Daehyon Kim

doi:10.58777/ise.v2i2.255

Authors

Daehyon Kim Chonnam National University

DOI:

https://doi.org/10.58777/ise.v2i2.255

Keywords:

Machine Learning, DBNs, Fuzzy ARTMAP, SVMs, Pattern Recognition, Predictive Accuracy

Abstract

The field of machine learning is rapidly advancing, and selecting the most suitable algorithm for predictive tasks remains a critical challenge. This study evaluates the predictive performance of three prominent machine learning algorithms: Deep Belief Networks (DBNs), Fuzzy ARTMAP, and Support Vector Machines (SVMs). Experiments on pattern recognition using image data from construction sites showed that DBNs achieved the highest predictive accuracy. In this study, experiments were conducted on a pattern recognition problem using image data from construction sites. The experimental results demonstrated that DBNs exhibited the highest predictive accuracy with the data used in this study. Algorithms such as DBNs, Fuzzy ARTMAP, and SVMs are representative models of machine learning methods, and their predictive power can vary depending on the type of data and the problem context. Therefore, future research should incorporate extended analyses with more diverse datasets and problem domains. Nonetheless, the findings of this study provide valuable guidelines for selecting appropriate algorithms for practical problem-solving and offer practical insights for practitioners aiming to optimize predictive accuracy across various machine learning applications.

Bidang pembelajaran mesin berkembang pesat, dan memilih algoritma yang paling sesuai untuk tugas-tugas prediktif masih merupakan tantangan penting. Studi ini memberikan evaluasi komprehensif terhadap kinerja prediktif dari tiga algoritma pembelajaran mesin terkemuka: Deep Belief Networks (DBNs), Fuzzy Adaptive Resonance Theory Mapping (FuzzyARTMAP), dan Support Vector Machines (SVMs). Dalam penelitian ini, percobaan dilakukan pada masalah pengenalan pola menggunakan data gambar dari lokasi konstruksi. Hasil eksperimen menunjukkan bahwa DBN menunjukkan akurasi prediksi tertinggi dibandingkan data yang digunakan dalam penelitian ini. Algoritma seperti DBN, FuzzyARTMAP, dan SVM merupakan model representatif dari metode pembelajaran mesin, dan kekuatan prediksinya dapat bervariasi bergantung pada jenis data dan konteks masalah. Oleh karena itu, penelitian di masa depan harus menggabungkan analisis yang diperluas dengan kumpulan data dan domain masalah yang lebih beragam. Meskipun demikian, temuan penelitian ini memberikan pedoman berharga dalam memilih algoritma yang tepat untuk pemecahan masalah praktis dan menawarkan wawasan praktis bagi para praktisi yang ingin mengoptimalkan akurasi prediksi di berbagai aplikasi pembelajaran mesin.

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