dc.contributor.author	Alabdulhadi, Mohammed H.	en
dc.contributor.author	Kannan, Vijayasarathy	en
dc.contributor.author	Soundarapandian, Manikandan	en
dc.contributor.author	Hamid, Tania	en
dc.date.accessioned	2014-05-08T15:58:53Z	en
dc.date.available	2014-05-08T15:58:53Z	en
dc.date.issued	2014-05-08	en
dc.description.abstract	Transfer learning unlike traditional machine learning is a technique that allows domains, tasks and distributions used in training and testing to be different. Knowledge gained from one domain can be utilized to learn a completely different domain. Ensemble computing portal is a digital library that contains resources, communities and technologies to aid in teaching. The major objective of this project is to apply the learning gained from the ACM Computing Classification System and classify educational YouTube videos so that they can be included in the Ensemble computing portal. Metadata of technical papers published in ACM are indexed in a SOLR server and we issue REST calls to retrieve the required metadata viz. title, abstract and general terms that we use to build the features. We make use of the ACM Computing Classification System 2012’s classification hierarchy to train our classifiers. We build classifiers for the level-2 and level-3 categories in the classification tree to help in classifying the educational YouTube videos. We utilize YouTube data API to search for educational videos in YouTube and retrieve the metadata including title, description and transcripts of the videos. These become the features of our test set. We specifically search for YouTube playlists that contain educational videos as we found out from our experience that neither a regular video search nor a search for videos in channels do retrieve relevant educational videos. We evaluate our classifiers using 10-fold cross-validation and present their accuracy in this report. With the classifiers built and trained using ACM metadata, we provide them the metadata that we collect from YouTube as the test data and manually evaluate the predictions. The results of our manual evaluation and the accuracy of our classifiers are also discussed. We identified that the ACM Computing Classification System’s hierarchy is sometimes ambiguous and YouTube metadata are not always reliable. These are the major factors that contribute to the reduced accuracy of our classifiers. In the future, we hope sophisticated natural language processing techniques can be applied to refine the features of both training and target data, which would help in improving the performance. We believe that more relevant metadata from YouTube in the form of transcripts and embedded text can be collected using sophisticated voice-to-text conversion and image retrieval algorithms respectively. This idea of transfer learning can also be extended to classify the presentation slides that are available in slideshare (http://www.slideshare.net) and also to classify certain educational blogs.	en
dc.description.sponsorship	The Project client is Chen, Yinlin (ylchen@vt.edu) who is a PHD candidate working in the Digital Library Research Laboratory at Virginia Tech	en
dc.identifier.uri	http://hdl.handle.net/10919/47922	en
dc.language.iso	en_US	en
dc.rights	Creative Commons Attribution-NonCommercial 3.0 United States	en
dc.rights.uri	http://creativecommons.org/licenses/by-nc/3.0/us/	en
dc.subject	Ensemble Classification	en
dc.subject	ACM CCS	en
dc.subject	YouTube Educational Videos	en
dc.subject	Transfer Learning	en
dc.subject	Text Classification	en
dc.title	Ensemble Classification Project	en
dc.type	Presentation	en
dc.type	Technical report	en

Ensemble Classification Project

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