FILTER-BASED FEATURE SELECTION PADA KATEGORISASI ARTIKEL BERITA BERBAHASA INDONESIA
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Date
2013-04-06
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Seminar Teknik Informatika dan Sistem Informasi 2013,Universitas Kristen Maranatha
Abstract
With the technology development, a large amount
of information such as news articles are available over the
internet. Hence, text categorization, such as applying
classification as one of data mining task, is needed. The major
issue in text categorization is the high dimensionality of data.
Therefore, we need to select some representative attributes to
improve the performance of text categorization. One of
techniques to complete this task is feature selection. Feature
selection can reduce high dimensionality. Thus, the classifier
effectiveness can improve. Among many method, is a filterbased
feature selection. This research examined and compared
some feature selection techniques toward Indonesian news
articles by applying filter model. These models are discussed:
Gini Index for text categorization, CHI, Information Gain,
Expected Cross Entropy, Weight Of Evidence and Orthogonal
Centroid Feature Selection (OCFS).
Description
Keywords
filter-based feature selection, measurement function