A Probabilistic Model for Classification of Multiple-Record Web Documents

This paper introduces a probabilistic approach to the classification and diagnosis of erroneous rules of operations that result from misconceptions (“bugs”) in a procedural domain of arithmetic. The model is different from the usual deterministic strategies common in the field of artificial intelligence because variability of response errors is explicitly treated through item response theory. As a concrete example, we analyze a dataset that reflects the use of erroneous rules of operation in problems of signed-number subtraction. The same approach, however, is applicable to the classification of several different groups of response patterns caused by a variety of different underlying misconceptions, different backgrounds of knowledge, or treatment.

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Gene selection for classification of cancers using probabilistic model building genetic algorithm

Biosystems ◽

10.1016/j.biosystems.2005.07.003 ◽

2005 ◽

Vol 82 (3) ◽

pp. 208-225 ◽

Cited By ~ 23

Author(s):

Topon Kumar Paul ◽

Hitoshi Iba

Keyword(s):

Genetic Algorithm ◽

Probabilistic Model ◽

Model Building ◽

Gene Selection ◽

Selection For

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Classification of Web Documents using Fuzzy Logic Categorical Data Clustering

IFIP The International Federation for Information Processing - Artificial Intelligence and Innovations 2007: from Theory to Applications ◽

10.1007/978-0-387-74161-1_11 ◽

2007 ◽

pp. 93-100 ◽

Cited By ~ 6

Author(s):

George E. Tsekouras ◽

Christos Anagnostopoulos ◽

Damianos Gavalas ◽

Economou Dafhi

Keyword(s):

Fuzzy Logic ◽

Categorical Data ◽

Data Clustering ◽

Web Documents ◽

Categorical Data Clustering

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Hierarchical Classification of Web Documents by Stratified Discriminant Analysis

Lecture Notes in Computer Science - Multidisciplinary Information Retrieval ◽

10.1007/978-3-642-31274-8_8 ◽

2012 ◽

pp. 94-108 ◽

Cited By ~ 5

Author(s):

Juan Carlos Gomez ◽

Marie-Francine Moens

Keyword(s):

Discriminant Analysis ◽

Hierarchical Classification ◽

Web Documents

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Restrictive Methods and Meta Methods for Thematically Focused Web Exploration

Handbook of Research on Web Information Systems Quality ◽

10.4018/978-1-59904-847-5.ch023 ◽

2011 ◽

pp. 405-423

Author(s):

Sergej Sizov ◽

Stefan Siersdorfer

Keyword(s):

Experimental Study ◽

Probabilistic Model ◽

Supervised Classification ◽

Unsupervised Clustering ◽

Use Case ◽

Clustering Methods ◽

Web Crawler ◽

Web Documents ◽

Case Examples ◽

Expert Search

This chapter addresses the problem of automatically organizing heterogeneous collections of Web documents for the generation of thematically-focused expert search engines and portals. As a possible application scenario for our techniques, we consider a focused Web crawler that aims to populate topics of interest by automatically categorizing newly-fetched documents. A higher accuracy of the underlying supervised (classification) and unsupervised (clustering) methods is achieved by leaving out uncertain documents rather than assigning them to inappropriate topics or clusters with low confidence. We introduce a formal probabilistic model for ensemble-based meta methods and explain how it can be used for constructing estimators and for quality-oriented tuning. Furthermore, we provide a comprehensive experimental study of the proposed meta methodology and realistic use-case examples.

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