71,612 research outputs found
Toward Optimal Feature Selection in Naive Bayes for Text Categorization
Automated feature selection is important for text categorization to reduce
the feature size and to speed up the learning process of classifiers. In this
paper, we present a novel and efficient feature selection framework based on
the Information Theory, which aims to rank the features with their
discriminative capacity for classification. We first revisit two information
measures: Kullback-Leibler divergence and Jeffreys divergence for binary
hypothesis testing, and analyze their asymptotic properties relating to type I
and type II errors of a Bayesian classifier. We then introduce a new divergence
measure, called Jeffreys-Multi-Hypothesis (JMH) divergence, to measure
multi-distribution divergence for multi-class classification. Based on the
JMH-divergence, we develop two efficient feature selection methods, termed
maximum discrimination () and methods, for text categorization.
The promising results of extensive experiments demonstrate the effectiveness of
the proposed approaches.Comment: This paper has been submitted to the IEEE Trans. Knowledge and Data
Engineering. 14 pages, 5 figure
Query generation from multiple media examples
This paper exploits an unified media document representation called feature terms for query generation from multiple media examples, e.g. images. A feature term refers to a value interval of a media feature. A media document is therefore represented by a frequency vector about feature term appearance. This approach (1) facilitates feature accumulation from multiple examples; (2) enables the exploration of text-based retrieval models for multimedia retrieval. Three statistical criteria, minimised chi-squared, minimised AC/DC rate and maximised entropy, are proposed to extract feature terms from a given media document collection. Two textual ranking functions, KL divergence and a BM25-like retrieval model, are adapted to estimate media document relevance. Experiments on the Corel photo collection and the TRECVid 2006 collection show the effectiveness of feature term based query in image and video retrieval
FSMJ: Feature Selection with Maximum Jensen-Shannon Divergence for Text Categorization
In this paper, we present a new wrapper feature selection approach based on
Jensen-Shannon (JS) divergence, termed feature selection with maximum
JS-divergence (FSMJ), for text categorization. Unlike most existing feature
selection approaches, the proposed FSMJ approach is based on real-valued
features which provide more information for discrimination than binary-valued
features used in conventional approaches. We show that the FSMJ is a greedy
approach and the JS-divergence monotonically increases when more features are
selected. We conduct several experiments on real-life data sets, compared with
the state-of-the-art feature selection approaches for text categorization. The
superior performance of the proposed FSMJ approach demonstrates its
effectiveness and further indicates its wide potential applications on data
mining.Comment: 8 pages, 6 figures, World Congress on Intelligent Control and
Automation, 201
KERT: Automatic Extraction and Ranking of Topical Keyphrases from Content-Representative Document Titles
We introduce KERT (Keyphrase Extraction and Ranking by Topic), a framework
for topical keyphrase generation and ranking. By shifting from the
unigram-centric traditional methods of unsupervised keyphrase extraction to a
phrase-centric approach, we are able to directly compare and rank phrases of
different lengths. We construct a topical keyphrase ranking function which
implements the four criteria that represent high quality topical keyphrases
(coverage, purity, phraseness, and completeness). The effectiveness of our
approach is demonstrated on two collections of content-representative titles in
the domains of Computer Science and Physics.Comment: 9 page
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