In this work, I examine a dataset of Amazon product metadata and propose a heterogeneous multiple classifier system for the task of identifying best-selling products in multiple categories. This system of classifiers consumes the product description and the featured product image as input and feeds them through binary classifiers of the following types: Convolutional Neural Network, Na¨ıve Bayes, Random Forest, Ridge Regression, and Support Vector Machine. While each individual model is largely successful in identifying best-selling products from non best-selling products and from worst-selling products, the multiple classifier system is shown to be stronger than any individual model in the majority of cases of identifying best-selling products from non best-selling products, and achieves up to 83.3% accuracy, depending on the product category. To my best knowledge, this research is the first application of ensemble learning to Amazon product data of this type and the first use of product images and Convolutional Neural Networks to predict product success.
","abstract_html":"<p>In this work, I examine a dataset of Amazon product metadata and propose a heterogeneous multiple classifier system for the task of identifying best-selling products in multiple categories. This system of classifiers consumes the product description and the featured product image as input and feeds them through binary classifiers of the following types: Convolutional Neural Network, Na¨ıve Bayes, Random Forest, Ridge Regression, and Support Vector Machine. While each individual model is largely successful in identifying best-selling products from non best-selling products and from worst-selling products, the multiple classifier system is shown to be stronger than any individual model in the majority of cases of identifying best-selling products from non best-selling products, and achieves up to 83.3% accuracy, depending on the product category. To my best knowledge, this research is the first application of ensemble learning to Amazon product data of this type and the first use of product images and Convolutional Neural Networks to predict product success.</p>","abstract_has_math":false,"creators":["Kranzlein, Michael"],"institution":null,"degree_name":"Master of Science in Computer Science (MSCS)","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Dan Chia-Tien Lo","Mingon Kang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-05-07T07:00:00Z","date_published":"2018-05-07T07:00:00Z","updated_at":"2026-07-24T02:43:26Z","subjects":["data analysis","ecommerce","image classification","machine learning","text classification","Other Computer Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.kennesaw.edu/cs_etd/18","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dan Chia-Tien Lo","Mingon Kang"]},{"key":"dc:creator","label":"Author","values":["Kranzlein, Michael"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-05-07T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Computer Science (MSCS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["data analysis","ecommerce","image classification","machine learning","text classification","Other Computer Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.kennesaw.edu/cs_etd/18"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this work, I examine a dataset of Amazon product metadata and propose a heterogeneous multiple classifier system for the task of identifying best-selling products in multiple categories. This system of classifiers consumes the product description and the featured product image as input and feeds them through binary classifiers of the following types: Convolutional Neural Network, Na¨ıve Bayes, Random Forest, Ridge Regression, and Support Vector Machine. While each individual model is largely successful in identifying best-selling products from non best-selling products and from worst-selling products, the multiple classifier system is shown to be stronger than any individual model in the majority of cases of identifying best-selling products from non best-selling products, and achieves up to 83.3% accuracy, depending on the product category. To my best knowledge, this research is the first application of ensemble learning to Amazon product data of this type and the first use of product images and Convolutional Neural Networks to predict product success.
"]},{"key":"dc:title","label":"Title","values":["A Multiple Classifier System for Predicting Best-Selling Amazon Products"]}]}],"canonical_facts":{"dc:contributor":["Dan Chia-Tien Lo","Mingon Kang"],"dc:creator":["Kranzlein, Michael"],"dc:date.available":["2018-05-07T07:00:00Z"],"dc:description.abstract":["In this work, I examine a dataset of Amazon product metadata and propose a heterogeneous multiple classifier system for the task of identifying best-selling products in multiple categories. This system of classifiers consumes the product description and the featured product image as input and feeds them through binary classifiers of the following types: Convolutional Neural Network, Na¨ıve Bayes, Random Forest, Ridge Regression, and Support Vector Machine. While each individual model is largely successful in identifying best-selling products from non best-selling products and from worst-selling products, the multiple classifier system is shown to be stronger than any individual model in the majority of cases of identifying best-selling products from non best-selling products, and achieves up to 83.3% accuracy, depending on the product category. To my best knowledge, this research is the first application of ensemble learning to Amazon product data of this type and the first use of product images and Convolutional Neural Networks to predict product success.
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