[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122951-en":3,"doc-seo-122951-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122951,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Research and Design of Machine Learning-based Product Classification System - MIS599","With the rapid development of big data, cloud computing, and the expansion of global e-commerce platforms, massive product data are generated continuously. Accurate information extraction and effective classification of these growing catalogs become essential for helping users find relevant products. This paper designs a machine learning-based product classification system by analyzing product data features, learning underlying patterns from known samples, and using the learned model to predict and classify unknown product data, improving accuracy and efficiency.","Research and Design of Machine Learning-based Product Classification  \nSystem  \nMIS599  \nJinghao Wang  \nAnthony Townsend, Chair  \nSubmitted 04/16/2024  \nTable of Contents  \nTable of Contents ..................................................................................................................................... 2  \nAbstract .................................................................................................................................................... 3  \nChapter 1 Introduction ............................................................................................................................ 4  \n1.1 Research Background Analysis...................................................................................................... 4  \n1.2 Current research status ................................................................................................................... 5  \n1.3 Organization of this article ............................................................................................................. 6  \nChapter 2 Existing technologies and methods for product classification ................................................ 7  \n2.1 Overview of Product Classification Systems ................................................................................. 7  \n2.2 Machine learning-based classification methods............................................................................. 8  \n2.3 Deep learning-based classification methods .................................................................................. 9  \n2.5 Analysis of product classification applications ............................................................................ 10  \nChapter 3 Data Acquisition and Data Cleaning of Product Data ........................................................... 10  \n3.1 Acquisition of Product Dataset .................................................................................................... 11  \n3.2 Data cleaning ................................................................................................................................ 11  \nChapter 4 Preprocessing of product data................................................................................................ 13  \n4.1 Segmentation methods ................................................................................................................. 13  \n4.2 Word segmentation optimization methods................................................................................... 16  \n4.3 Feature vectorization .................................................................................................................... 17  \nChapter 5 Decision tree algorithm and random forest algorithm research and optimization ................ 22  \n5.1 Bagging algorithm ........................................................................................................................ 22  \n5.2 Decision tree algorithm ................................................................................................................ 24  \n5.3 Random forest algorithm.............................................................................................................. 26  \nChapter 6 Product Classification System ............................................................................................... 28  \n6.1 System requirements analysis ...................................................................................................... 28  \n6.2 The system uses tools ................................................................................................................... 29  \n6.3 Detailed design and implementation of system modules ............................................................. 31  \n6.4 System testing .............................................................................................................................. 36  \nChapter 7 Experiment Results and ","cbCaichbigkC2prh","https://ap.wps.com/l/cbCaichbigkC2prh","pdf",2331419,1,46,"English","en",105,"# Abstract\n# Chapter 1 Introduction\n## Research Background Analysis\n## Current research status\n## Organization of this article\n# Chapter 2 Existing technologies and methods for product classification\n## Overview of Product Classification Systems\n## Machine learning-based classification methods\n## Deep learning-based classification methods\n## Analysis of product classification applications\n# Chapter 3 Data Acquisition and Data Cleaning of Product Data\n## Acquisition of Product Dataset\n## Data cleaning\n# Chapter 4 Preprocessing of product data\n## Segmentation methods\n## Word segmentation optimization methods\n## Feature vectorization\n# Chapter 5 Decision tree algorithm and random forest algorithm research and optimization\n## Bagging algorithm\n## Decision tree algorithm\n## Random forest algorithm\n# Chapter 6 Product Classification System\n## System requirements analysis\n## The system uses tools\n## Detailed design and implementation of system modules\n## System testing\n# Chapter 7 Experiment Results and Analysis\n## Comparative Experiment on Data Imbalance\n## Areas for Improvement\n## Application Prospects\n## Concluding remarks\n# References","[{\"question\":\"Why is product classification important in e-commerce?\",\"answer\":\"Product classification is critical because e-commerce platforms generate massive product catalogs, and users need relevant products found accurately and quickly. Manual classification cannot scale with the growing quantity and variety of products.\"},{\"question\":\"How does the proposed approach classify unknown products?\",\"answer\":\"The system analyzes product data features to discover underlying patterns and then uses these patterns to predict and classify unknown product data. The model learns from known samples and applies learned knowledge to new items.\"},{\"question\":\"What role do preprocessing steps like segmentation and feature vectorization play?\",\"answer\":\"Preprocessing prepares raw product data for learning by segmenting text, optimizing word segmentation, and converting processed data into feature vectors. These steps improve the quality of inputs to classification algorithms.\"}]","Research and Design of Machine Learning-based Product Classification System - MIS599 | PDF",1785813855,116,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"research-and-design-of-machine-learning-based-product-classification-system-mis599","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/research-and-design-of-machine-learning-based-product-classification-system-mis599/122951/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is product classification important in e-commerce?","Question",{"text":75,"@type":76},"Product classification is critical because e-commerce platforms generate massive product catalogs, and users need relevant products found accurately and quickly. Manual classification cannot scale with the growing quantity and variety of products.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed approach classify unknown products?",{"text":80,"@type":76},"The system analyzes product data features to discover underlying patterns and then uses these patterns to predict and classify unknown product data. The model learns from known samples and applies learned knowledge to new items.",{"name":82,"@type":73,"acceptedAnswer":83},"What role do preprocessing steps like segmentation and feature vectorization play?",{"text":84,"@type":76},"Preprocessing prepares raw product data for learning by segmenting text, optimizing word segmentation, and converting processed data into feature vectors. 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