Uncertainly measures of rough set prediction
Ivo Düntsch,Günther Gediga +1 more
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TLDR
This work presents three model selection criteria, using information theoretic entropy in the spirit of the minimum description length principle, based on the principle of indifference combined with the maximum entropy principle, thus keeping external model assumptions to a minimum.About:
This article is published in Artificial Intelligence.The article was published on 1998-11-01 and is currently open access. It has received 403 citations till now. The article focuses on the topics: Dominance-based rough set approach & Principle of maximum entropy.read more
Citations
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Rudiments of rough sets
Zdziasław Pawlak,Andrzej Skowron +1 more
TL;DR: The basic concepts of rough set theory are presented and some rough set-based research directions and applications are pointed out, indicating that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences.
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Qualitative Spatial Representation and Reasoning: An Overview
TL;DR: The paper is a overview of the major qualitative spatial representation and reasoning techniques including ontological aspects, topology, distance, orientation and shape, and qualitative spatial reasoning including reasoning about spatial change.
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MGRS: A multi-granulation rough set
TL;DR: It is shown that some of the properties of Pawlak's rough set theory are special instances of those of MGRS, and several important measures are presented, which are re-interpreted in terms of a classic measure based on sets, the Marczewski-Steinhaus metric and the inclusion degree measure.
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Positive approximation: An accelerator for attribute reduction in rough set theory
TL;DR: A theoretic framework based on rough set theory, called positive approximation, is introduced, which can be used to accelerate a heuristic process of attribute reduction, and several representative heuristic attribute reduction algorithms inrough set theory have been enhanced.
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Attribute reduction in decision-theoretic rough set models
TL;DR: This paper addresses attribute reduction in decision-theoretic rough set models regarding different classification properties, such as decision-monotocity, confidence, coverage, generality and cost, and provides a new insight into the problem of attribute reduction.
References
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Information Theory and Statistical Mechanics. II
TL;DR: In this article, the authors consider statistical mechanics as a form of statistical inference rather than as a physical theory, and show that the usual computational rules, starting with the determination of the partition function, are an immediate consequence of the maximum-entropy principle.
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Rough sets
TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
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Paper: Modeling by shortest data description
TL;DR: The number of digits it takes to write down an observed sequence x1,...,xN of a time series depends on the model with its parameters that one assumes to have generated the observed data.
Book
An Introduction to Kolmogorov Complexity and Its Applications
Ming Li,Paul M. B. Vitányi +1 more
TL;DR: The Journal of Symbolic Logic as discussed by the authors presents a thorough treatment of the subject with a wide range of illustrative applications such as the randomness of finite objects or infinite sequences, Martin-Loef tests for randomness, information theory, computational learning theory, the complexity of algorithms, and the thermodynamics of computing.