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The max-min hill-climbing Bayesian network structure learning algorithm

• Ioannis Tsamardinos, Laura E. Brown, Constantin F. Aliferis
• Machine Learning
• 2006

We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and search-and-score… (More)

Algorithms for Large Scale Markov Blanket Discovery

• Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov
• FLAIRS Conference
• 2003

This paper presents a number of new algorithms for discovering the Markov Blanket of a target variable T from training data. The Markov Blanket can be used for variable selection for classification,… (More)

Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation

• Constantin F. Aliferis, Alexander R. Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos
• Journal of Machine Learning Research
• 2010

We present an algorithmic framework for learning local caus al structure around target variables of interest in the form of direct causes/effects and Markov bla nkets applicable to very large data… (More)

A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis

• Alexander R. Statnikov, Constantin F. Aliferis, Ioannis Tsamardinos, Douglas P. Hardin, Shawn Levy
• Bioinformatics
• 2005

MOTIVATION Cancer diagnosis is one of the most important emerging clinical applications of gene expression microarray technology. We are seeking to develop a computer system for powerful and reliable… (More)

Towards Principled Feature Selection: Relevancy, Filters and Wrappers.

• Ioannis Tsamardinos, Constantin F. Aliferis
• AISTATS
• 2003

HITON: A Novel Markov Blanket Algorithm for Optimal Variable Selection

• Constantin F. Aliferis, Ioannis Tsamardinos, Alexander R. Statnikov
• AMIA
• 2003

UNLABELLED We introduce a novel, sound, sample-efficient, and highly-scalable algorithm for variable selection for classification, regression and prediction called HITON. The algorithm works by… (More)

CTP: A New Constraint-Based Formalism for Conditional, Temporal Planning

• Ioannis Tsamardinos, Thierry Vidal, Martha E. Pollack
• Constraints
• 2003

Temporal constraints pose a challenge for conditional planning, because it is necessary for a conditional planner to determine whether a candidate plan will satisfy the specified temporal… (More)

Reformulating temporal plans for efficient execution

• Nicola Muscettola, Paul H. Morris, Ioannis Tsamardinos
• KR 1998
• 1998

The Simple Temporal Network formalism permits signiicant exibility in specifying the occurrence time of events in temporal plans. However, to retain this exibility during execution , there is a need… (More)

Time and sample efficient discovery of Markov blankets and direct causal relations

• Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov
• KDD
• 2003

Data Mining with Bayesian Network learning has two important characteristics: under conditions learned edges between variables correspond to casual influences, and second, for every variable T in the… (More)

Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions

• Constantin F. Aliferis, Alexander R. Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos
• Journal of Machine Learning Research
• 2010

In part I of this work we introduced and evaluated the G neralized Local Learning (GLL) framework for producing local causal and Markov blanket inductio n algorithms. In the present second part we… (More)

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