Attribute domain
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Attribute reduction in interval-valued information systems based on information entropies Open
Interval-valued data appear as a way to represent the uncertainty affecting the observed values. Dealing with interval-valued information systems is helpful to generalize the applications of rough set theory. Attribute reduction is a key i…
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An Attribute Reduction Method Using Neighborhood Entropy Measures in Neighborhood Rough Sets Open
Attribute reduction as an important preprocessing step for data mining, and has become a hot research topic in rough set theory. Neighborhood rough set theory can overcome the shortcoming that classical rough set theory may lose some usefu…
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Semi-supervised attribute reduction based on label distribution and label irrelevance Open
Attribute reduction in partially labeled data, also called semi-supervised attribute reduction, is an important issue. In recent years, the research on semi-supervised attribute reduction has attracted the attention of many scholars. Unfor…
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Sensing Attribute Weights: A Novel Basic Belief Assignment Method Open
Dempster–Shafer evidence theory is widely used in many soft sensors data fusion systems on account of its good performance for handling the uncertainty information of soft sensors. However, how to determine basic belief assignment (BBA) is…
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Multi-attribute Decision Making Based on Z-valuation Open
In this paper we investigate multi-attribute decision making problem, where the attribute values are Z-numbers, and the weight information on attributes are partially reliable. The presented method is based on overall criteria positive ide…
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Venture capital project selection based on interval number grey target decision model Open
In this study, aiming at the multi-attribute decision-making problem with incomplete and uncertain attribute weight information and attribute value of interval numbers, a grey target decision-making model of interval numbers based on posit…
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An Efficient Social Attribute Inference Scheme Based on Social Links and Attribute Relevance Open
Social network is a critical component in mobile multimedia systems, where users share their videos, photos, and other media. However, the information (e.g., posts, user profiles, etc.) shared on the social network platforms usually reflec…
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Het2Hom: Representation of Heterogeneous Attributes into Homogeneous Concept Spaces for Categorical-and-Numerical-Attribute Data Clustering Open
Data sets composed of a mixture of categorical and numerical attributes (also called mixed data hereinafter) are common in real-world cluster analysis. However, insightful analysis of such data under an unsupervised scenario using clusteri…
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Dominance-Based Neighborhood Rough Sets and Its Attribute Reduction Open
In real-life applications, partial order may exist in the domain of attributes and different data types often coexist in an decision system. Dominance-based rough set approach has been used widely in multi-attribute and multi-criteria deci…
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Learning Hypergraph-regularized Attribute Predictors Open
We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. Then the attribute prediction problem is casted as a regular…
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Entropy Measures for Plithogenic Sets and Applications in Multi-Attribute Decision Making Open
Plithogenic set is an extension of the crisp set, fuzzy set, intuitionistic fuzzy set, and neutrosophic sets, whose elements are characterized by one or more attributes, and each attribute can assume many values. Each attribute has a corre…
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Feature Selection for Multi-Label Learning Based on F-Neighborhood Rough Sets Open
Multi-label learning is often applied to handle complex decision tasks, and feature selection is its essential part. The relation of labels is always ignored or not enough to consider for both multi-label learning and its feature selection…
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Coevolutionary Fuzzy Attribute Order Reduction With Complete Attribute-Value Space Tree Open
Since big data sets are structurally complex, high-dimensional, and their attributes exhibit some redundant and irrelevant information, the selection, evaluation, and combination of those large-scale attributes pose huge challenges to trad…
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An Improved ID3 Decision Tree Algorithm Based on Attribute Weighted Open
ID3 decision tree algorithm uses information gain selection splitting attribute tend to choose the more property values, and the number of attribute values can not be used to measure the attribute importance, in view of the above problems,…
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Metric Based Attribute Reduction Method in Dynamic Decision Tables Open
Feature selection is a vital problem which needs to be effectively solved in knowledge discovery in databases and pattern recognition due to two basic reasons: minimizing costs and accurately classifying data. Feature selection using rough…
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A Novel Strategy for Minimum Attribute Reduction Based on Rough Set Theory and Fish Swarm Algorithm Open
For data mining, reducing the unnecessary redundant attributes which was known as attribute reduction (AR), in particular, reducts with minimal cardinality, is an important preprocessing step. In the paper, by a coding method of combinatio…
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Fully-Featured Attribute Transfer Open
Image attribute transfer aims to change an input image to a target one with expected attributes, which has received significant attention in recent years. However, most of the existing methods lack the ability to de-correlate the target at…
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Incremental Attribute Reduction Method Based on Chi-Square Statistics and Information Entropy Open
Attributes in datasets are usually not equally significant. Some attributes are unnecessary or redundant. Attribute reduction is an important research issue of rough set theory, which can find minimum subsets of attributes with the same cl…
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A Moderate Attribute Reduction Approach in Decision-Theoretic Rough Set Open
Attribute reduction is an important topic in Decision-Theoretic Rough Set theory. To overcome the limitations of lower-approximation-monotonicity based reduct and cost minimum based reduct, a moderate attribute reduction approach is propos…
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Attribute Reduction in an Incomplete Interval-Valued Decision Information System Open
An incomplete interval-valued decision information system (IIVDIS) is a significant type of data decision table, which is ubiquitous in real life. Interval value is a form of knowledge representation, and it seems to be an embodiment of th…
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Analysis of Service Quality on Customer Satisfaction Through Importance Performance Analysis and KANO Model Open
Customer satisfaction is the level at which the perceived performance of the product will match the expectations of a customer. PT. Astra International Tbk-Daihatsu is a company that runs in the automotive field This study was conducted in…
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Face Attribute Modification Using Fine-Tuned Attribute-Modification Network Open
Multi-domain image-to-image translation with the desired attributes is an important approach for modifying single or multiple attributes of a face image, but is still a challenging task in the computer vision field. Previous methods were b…
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An Approach to Determining Attribute Weights Based on Integrating Preference Information on Attributes with Decision Matrix Open
The interval multiple attribute decision-making problems are studied in this paper, where the preference information on attributes is expressed with preference orderings, linguistic terms, interval numbers, and inequality constraints among…
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About a Fuzzy Distance between Two Fuzzy Partitions and Application in Attribute Reduction Problem Open
According to traditional rough set theory approach, attribute reduction methods are performed on the decision tables with the discretized value domain, which are decision tables obtained by discretized data methods. In recent years, resear…
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On Distance-Based Attribute Reduction With <i>α</i>, <i>β</i>-Level Intuitionistic Fuzzy Sets Open
Attribute reduction, often referred to as feature selection, is a vital step in data preprocessing aimed at eliminating unnecessary attributes and enhancing the efficiency of classification models. Intuitionistic fuzzy sets are widely ackn…
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Information Entropy-Based Attribute Reduction for Incomplete Set-Valued Data Open
This paper investigates attribute reduction for incomplete set-valued data based on information entropy. The similarity degree between information values on a conditional attribute of an incomplete set-valued decision information system (I…
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Crop Evaluation System Optimization: Attribute Weights Determination Based on Rough Sets Theory Open
The present study is mainly a continuation of our previous study, which is about a crop evaluation system development that is based on grey relational analysis. In that system, the attribute weight determination affects the evaluation resu…
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Multi‐granularity decision rough set attribute reduction algorithm under quantum particle swarm optimization Open
The existing attribute reductions are carried out using equivalence relations under a complete information system, and there is less research on attribute reductions of incomplete information systems with new theoretical models such as mul…
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A Novel Attribute Reduction Algorithm for Incomplete Information Systems Based on a Binary Similarity Matrix Open
With databases growing at an unrelenting rate, it may be difficult and complex to extract statistics by accessing all of the data in many practical problems. Attribute reduction, as an effective method to remove redundant attributes from m…
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A Fast Attribute Reduction Algorithm Based on a Positive Region Sort Ascending Decision Table Open
Attribute reduction is one of the challenging problems in rough set theory. To accomplish an efficient reduction algorithm, this paper analyzes the shortcomings of the traditional methods based on attribute significance, and suggests a nov…