Web Içerik Siniflandirmasi Için Makine Öǧrenmesi

Translated title of the contribution: Machine Learning for Web Content Classification

Kenan Enes Aydin, Sefer Baday

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

Categorization of web sites is an important problem and has many practical applications. One such application is parental control for safe internet for children. Failure to classify websites by specific rules makes it difficult to access information, as well as leaving many users of different age groups with the harmful side of the Internet. Current secure internet solutions are not comprehensive or cannot be customized. Furthermore, the fact that the blocking orders issued by the courts do not cover all harmful sites and these websites change their domains so often. Thus, dynamic classification of websites using the text data is very important. In this study, using natural language processing and machine learning techniques websites are classified. Content of web sites from various languages are collected and preprocessed before applying machine learning techniques. In the study, 17 classes were used, the highest classification success was 0.8756 and this result was reached by the SVM method.

Translated title of the contributionMachine Learning for Web Content Classification
Original languageTurkish
Title of host publicationProceedings - 2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728191362
DOIs
Publication statusPublished - 15 Oct 2020
Externally publishedYes
Event2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020 - Istanbul, Turkey
Duration: 15 Oct 202017 Oct 2020

Publication series

NameProceedings - 2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020

Conference

Conference2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020
Country/TerritoryTurkey
CityIstanbul
Period15/10/2017/10/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

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