Cost-Sensitive Feature Selection of Numeric Data with Measurement Errors
Journal of Applied Mathematics, 2013 Feature selection is an essential process in data mining applications since it reduces a model’s complexity. However, feature selection with various types of costs is still a new research topic.
Hong Zhao, Fan Min, William Zhu
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On mesogranulation, network formation and supergranulation [PDF]
, 2000 We present arguments which show that in all likelihood mesogranulation is not a true scale of solar convection but the combination of the effects of both highly energetic granules, which give birth to strong positive divergences (SPDs) among which we ...
Berger +30 more
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Matroidal Structure of Rough Sets Based on Serial and Transitive Relations
Journal of Applied Mathematics, 2012 The theory of rough sets is concerned with the lower and upper approximations of objects through a binary relation on a universe. It has been applied to machine learning, knowledge discovery, and data mining. The theory of matroids is a generalization of
Yanfang Liu, William Zhu
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Supervised Learning via Unsupervised Sparse Autoencoder
IEEE Access, 2018 Dimensionality reduction is commonly used to preprocess high-dimensional data, which is an essential step in machine learning and data mining. An outstanding low-dimensional feature can improve the efficiency of subsequent learning tasks.
Jianran Liu, Chan Li, Wenyuan Yang
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Geometric Lattice Structure of Covering-Based Rough Sets through Matroids
Journal of Applied Mathematics, 2012 Covering-based rough set theory is a useful tool to deal with inexact, uncertain, or vague knowledge in information systems. Geometric lattice has been widely used in diverse fields, especially search algorithm design, which plays an important role in ...
Aiping Huang, William Zhu
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Distributed Decisions on TV Spectrum Allocation Considering Spatial and Temporal Variation
IEEE Access, 2018 TV spectrum has lower path loss, longer transmission range, and higher penetration capability, resulting in a wide range of potential important applications.
Zhenwei Chen +4 more
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Geometric Lattice Structure of Covering and Its Application to Attribute Reduction through Matroids
Journal of Applied Mathematics, 2014 The reduction of covering decision systems is an important problem in data mining, and covering-based rough sets serve as an efficient technique to process the problem.
Aiping Huang, William Zhu
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Deformation-Driven Diffusion and Plastic Flow in Two-Dimensional Amorphous Granular Pillars [PDF]
, 2015 We report a combined experimental and simulation study of deformation-induced diffusion in compacted two-dimensional amorphous granular pillars, in which thermal fluctuations play negligible role.
Durian, Douglas J. +4 more
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A Variable Precision Covering-Based Rough Set Model Based on Functions
The Scientific World Journal, 2014 Classical rough set theory is a technique of granular computing for handling the uncertainty, vagueness, and granularity in information systems. Covering-based rough sets are proposed to generalize this theory for dealing with covering data.
Yanqing Zhu, William Zhu
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Applications of Matrices to a Matroidal Structure of Rough Sets
Journal of Applied Mathematics, 2013 Rough sets provide an efficient tool for dealing with the vagueness and granularity in information systems. They are widely used in attribute reduction in data mining. There are many optimization issues in attribute reduction.
Jingqian Wang, William Zhu
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