Abstract
The task of automatically identify body type with high accuracy is still a relevant problem in clothing fashion settings. This paper addresses such problem, presenting a study on machine learning techniques applied to classify women’s body shapes, taking into account a small set of body attributes, in order to further find appropriate clothing models. Thus, we perform a comparative study on such techniques to evaluate the accuracy of four classifiers, aiming at selecting the best of them to be used for clothing model recommendation based on rules. Overall, in the conducted computational experiment, Random Forest and SVM methods had the best performance, but the other two had also very good results, demonstrating their effectiveness to automatically identifying body type, serving as a relevant information to be used in our rule-based system to provide clothing model recommendation.
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Costa, E., Silva, E., Rocha, H., Maia, A., Vieira, T. (2018). Study on Machine Learning Algorithms to Automatically Identifying Body Type for Clothing Model Recommendation. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 747. Springer, Cham. https://doi.org/10.1007/978-3-319-77700-9_8
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DOI: https://doi.org/10.1007/978-3-319-77700-9_8
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