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Binary classification based on a combination of rough set theory and decision trees
INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, 2023 The subject of the study is to improve the accuracy and efficiency of classification algorithms using decision trees by integrating the principles of Rough Set theory, a mathematical approach to approximating sets. The aim of the study is to develop a hybrid model that integrates rough set theory with decision tree algorithms, thereby solving the ...
Dmytro Chernyshov, Dmytro Sytnikov
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A characterization of trees with equal 2-domination and 2-independence numbers [PDF]
Discrete Mathematics & Theoretical Computer Science, 2017 A set $S$ of vertices in a graph $G$ is a $2$-dominating set if every vertex of $G$ not in $S$ is adjacent to at least two vertices in $S$, and $S$ is a $2$-independent set if every vertex in $S$ is adjacent to at most one vertex of $S$.
Christoph Brause +2 more
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Decision Tree Construction based on Rough Set Theory under Characteristic Relation [PDF]
Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007), 2007 Several approaches based on rough set have been proposed for constructing decision tree in complete information systems. In fact, many information systems are incomplete in practical applications. In this paper, a new algorithm, Decision Tree Construction based on Rough Set Theory under Characteristic Relation (DTCRSCR), is proposed for mining ...
Jianhuai Qi +3 more
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Quantification of Temporal Fault Trees Based on Fuzzy Set Theory [PDF]
, 2014 Fault tree analysis (FTA) has been modified in different ways to make it capable of performing quantitative and qualitative safety analysis with temporal gates, thereby overcoming its limitation in capturing sequential failure behaviour. However, for many systems, it is often very difficult to have exact failure rates of components due to increased ...
Gordon, Neil +3 more
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Data mining of the GAW14 simulated data using rough set theory and tree-based methods [PDF]
BMC Genetics, 2005 Abstract Rough set theory and decision trees are data mining methods used for dealing with vagueness and uncertainty. They have been utilized to unearth hidden patterns in complicated datasets collected for industrial processes. The Genetic Analysis Workshop 14 simulated data were generated using a system that implemented multiple ...
Wei, Liang-Ying +2 more
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Event Tree Analysis of Xinda Oil Pipeline Leakage Based on Fuzzy Set Theory [PDF]
The Open Civil Engineering Journal, 2017 Event tree is a logical analysis of probability for outcome events by initiating event and probable subsequent events. However, the evaluation of every single event may contain vague information or cannot be described by a crisp number of probabilities in application.
Di Wang, Enbin Liu, Liyu Huang
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Enhancing the Color Set Partitioning in Hierarchical Tree (SPIHT) Algorithm Using Correlation Theory [PDF]
Journal of Computer Science, 2011 Problem statement: Efficient color image compression algorithm is essential for mass storage and the transmission of the image. The compression efficiency of the Set Partitioning in Hierarchical Tree (SPIHT) coding algorithm for color images is improved by using correlation theory.
Santhi
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A Decision Tree Algorithm Based on Rough Set Theory after Dimensionality Reduction
International Journal of Computer Applications, 2011 Decision tree technology has proven to be a valuable way of capturing human decision making within a computer. As ID3 select those attribute as splitting attributes which have different values whether it classify dataset properly or not. There is another drawback of ID3 which repeat sub tree many times and select same attribute many times.
Manisha Tantuway +1 more
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Constructing Minimal Spanning Tree Based on Rough Set Theory for Gene Selection
International Journal of Artificial Intelligence & Applications, 2012 Microarray gene dataset often contains high dimensionalities which cause difficulty in clustering and classification. Datasets containing huge number of genes lead to increased complexity and therefore, degradation of dataset handling performance. Often, all the measured features of these high-dimensional datasets are not relevant for understanding the
Soumen Kumar Pati
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Granulation Using Clustering And Rough Set Theory And Its Tree Representation
, 2007 Granular computing deals with representation of information in the form of some aggregates and related methods for transformation and analysis for problem solving. A granulation scheme based on clustering and Rough Set Theory is presented with focus on structured conceptualization of information has been presented in this paper.
Girish Kumar Singh, Sonajharia Minz
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