Python Machine Learning. Dry Beans Classification Case

• Autorzy: Słowiński Grzegorz

Identyfikatory

DOI

10.26348/znwwsi.30.7

• Języki publikacji: EN

Abstrakty

EN

A dataset containing over 13k samples of dry beans geometric features was analyzed using machine learning (ML) and deep learning (DL) techniques with the goal to automatically classify the bean species. Performance in terms of accuracy, train and test time was analyzed. First the original dataset was reduced to eliminate redundant features (too strongly correlated and echoing others). Then the dataset was visualized and analyzed with a few shallow learning techniques and simple artificial neural network. Cross validation was used to check the learning process repeatability. Influence of data preparation (dimension reduction) on shallow learning techniques were observed. In case of Multilayer Perceptron 3 activation functions were tried: ReLu, ELU and sigmoid. Random Forest appeared to be the best model for dry beans classification task reaching average accuracy reaching 92.61% with reasonable train and test times.

Słowa kluczowe

EN

machine learning; deep learning; data dimension reduction; activation; function

• Wydawca: Warszawska Wyższa Szkoła Informatyki
• Czasopismo: Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
• Rocznik: 2024
• Tom: nr 30
• Strony: 7--26
• Opis fizyczny: Bibliogr. 13 poz., rys., tab., wykr.

Twórcy

autor

Słowiński Grzegorz

• Warsaw School of Computer Science, Warsaw, Poland

Bibliografia

• [1] B. Schroeder. (2021) The Data Analytics Profession And Employment Is Exploding - Three Trends That Matter. [Online]. https://www.forbes.com/sites/bernhardschroeder/2021/06/11/the-data-analytics-profession-and-employment-is-exploding-three-trends-that-matter/?sh=589c02c33f81.
• [2] 9 Top Programming Languages for Data Science. [Online].https://www.edx.org/resources/9-top-programming-languages-for-data-science.
• [3] J. McCarthy, M. L. Minsky, N. Rochester, and C. E. Shannon. (1955) A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. [Online]. http://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf.
• [4] T. Zalewski, “Definicja sztucznej inteligencji,” [In:] Prawo sztucznej inteligencji, L. Lai and M. Świerczyński (Eds.). Warszawa: C.H. Beck, 2020.
• [5] Artificial Intelligence [in:] Oxford Reference. [Online]. https://www.oxfordreference.com/display/10.1093/oi/authority.20110803095426960.
• [6] A. Kaplan and M. Haenlein, “Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence,” Business Horizons, Vol. 62, No. 1, pp. 15-25, 2019. [Online]. https://doi.org/10.1016/j.bushor.2018.08.004.
• [7] F. Chollet, Deep Learning with Python, 2nd ed. Shelter Island, NY: Manning Publications Co., 2021. [Online]. https://www.academia.edu/download/62075089/Francois_Chollet-Deep_Learning_with_Python-Manning_2018_120200212-91819-unzepp.pdf.25
• [8] W. Cukierski. (2013) Dogs vs. Cats. [Online]. Kaggle. https://kaggle.com/competitions/dogs-vs-cats.
• [9] [Online]. https://github.com/stophouse/DryBeansPostProceedings.
• [10] Dry Bean Dataset. (2020) UCI Machine Learning Repository. [Online]. https://doi.org/10.24432/C50S4B.
• [11] M. Koklu and I. A. Ozkan, “Multiclass classification of dry beans using computer vision and machine learning techniques,” Computers and Electronics in Agriculture, Vol. 174, p. 105507, 2020. [Online]. https://doi.org/10.1016/j.compag.2020.105507.
• [12] J. VanderPlas, Python Data Science Handbook. Sebastopol, CA: O’Reilly Media, Inc., 2017. [Online]. https://www.academia.edu/download/62974298/pythondatasciencehandbook20200415-66956-1noqwv2.pdf.
• [13] A. Géron, Hands on Machine Learning with Scikit Learn, Keras, 2nd ed. Sebastopol, CA: O’Reilly Media, 2019. [Online]. http://14.139.161.31/OddSem-0822-1122/Hands-On_Machine_Learning_with_Scikit-Learn-Keras-and-TensorFlow-2nd-Edition-Aurelien-Geron.pdf.