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Overview:

This paper describes a machine learning approach for detecting web spam. Each example in this classification task corresponds to 100 web pages from a host and the task is to predict whether this collection of pages represents spam or not. Their approach begins by adding several human-engineered features constructed from the raw data. They then construct a rough classifier and use semi-supervised learning to classify the unlabelled examples provided to them. They then construct additional link-based features and incorporate them into the training process. They also employ a combinatorial feature-fusion method for "Compressing" the enormous number of word-based features that are available, so that conventional machine learning algorithms can be used. Their results demonstrate the effectiveness of semi-supervised learning and the combinatorial feature-fusion method.

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Format:PDFSize:158 KB
Date:Jul 2007
Pages:8

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