Shalendra Chhabra

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2ranked-venue papers

1as first author

0since 2021 · last 2004

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-authorDatabases, data management, data science and information retrieval · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence 1 paper		Probabilistic and Bayesian machine learning · 100%
Databases, data mining, and information retrieval 1 paper		Data mining · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Machine learning › Probabilistic and Bayesian machine learning › structured models graphical models	1	2004	Spam Filtering using a Markov Random Field Model with Variable Weighting Schemas · ICDM 2004
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models markov random field	1	2004	Spam Filtering using a Markov Random Field Model with Variable Weighting Schemas · ICDM 2004
Data mining › text mining › text classification email classification	1	2004	Spam Filtering using a Markov Random Field Model with Variable Weighting Schemas · ICDM 2004
Data mining › anomaly detection spam detection	1	2004	Spam Filtering using a Markov Random Field Model with Variable Weighting Schemas · ICDM 2004

Methods — techniques the papers use, named apart from their topics

markov random field · 0.1weighting schemes · 0.0weighting scheme · 0.0

Year	Publication	Venue	Position
2004	Spam Filtering using a Markov Random Field Model with Variable Weighting Schemas abstract In this paper we present a Markov random field model based approach to filter spam. Our approach examines the importance of the neighborhood relationship (MRF cliques) among words in an email message for the purpose of spam classification. We propose and test several different theoretical bases for weighting schemes among corresponding neighborhood windows. Our results demonstrate that unexpected side effects depending on the neighborhood window size may have larger accuracy impact than the neighborhood relationship effects of the Markov random field. Shalendra Chhabra, William Yerazunis, Christian Siefkes	ICDM	1
2004	Combining Winnow and Orthogonal Sparse Bigrams for Incremental Spam Filtering Christian Siefkes, Fidelis Assis, Shalendra Chhabra, William Yerazunis	PKDD	3