Improved support vector machine algorithm for heterogeneous data

Peng, Shili; Hu, Qinghua; Chen, Yinli; Dang, Jianwu

doi:10.1016/j.patcog.2014.12.015

Cited by 42 publications

(19 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…What can still be mentioned is the method of probabilistic classifiers [25,26], or the recently popular method of support vectors [27][28][29][30]. Each of these methods has its advantages, but also limitations.…”

Section: Discussionmentioning

confidence: 99%

Methods of Computational Intelligence in the Context of Quality Assurance in Foundry Products

Rojek

Regulski

Wilk-Kołodziejczyk

et al. 2016

Archives of Foundry Engineering

View full text Add to dashboard Cite

One way to ensure the required technical characteristics of castings is the strict control of production parameters affecting the quality of the finished products. If the production process is improperly configured, the resulting defects in castings lead to huge losses. Therefore, from the point of view of economics, it is advisable to use the methods of computational intelligence in the field of quality assurance and adjustment of parameters of future production. At the same time, the development of knowledge in the field of metallurgy, aimed to raise the technical level and efficiency of the manufacture of foundry products, should be followed by the development of information systems to support production processes in order to improve their effectiveness and compliance with the increasingly more stringent requirements of ergonomics, occupational safety, environmental protection and quality. This article is a presentation of artificial intelligence methods used in practical applications related to quality assurance. The problem of control of the production process involves the use of tools such as the induction of decision trees, fuzzy logic, rough set theory, artificial neural networks or case-based reasoning.

show abstract

Section: Discussionmentioning

confidence: 99%

Methods of Computational Intelligence in the Context of Quality Assurance in Foundry Products

Rojek

Regulski

Wilk-Kołodziejczyk

et al. 2016

Archives of Foundry Engineering

View full text Add to dashboard Cite

show abstract

“…In addition, another set of methods, e.g., [14], [16], [29], [30], [31], [32], [33], learns metrics by using side-information such as labels, to guide metric learning for categorical data. The work in [29] is the first to consider label information for categorical data similarity, which uses labels to divide data into subsets and considers the attribute value distribution within these subsets.…”

Section: Related Workmentioning

confidence: 99%

“…However, KDML suffers from information loss due to only using the matching method to capture the relationships in the data. Instead of considering class as side information, the method in [33] captures the class information and classification model information simultaneously. However, it only maps a categorical value to a numerical value, which cannot well represent a categorical value when it has high-dimensional embedding.…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous Metric Learning of Categorical Data with Hierarchical Couplings

Zhu

Cao

Liu

et al. 2018

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

Learning appropriate metric is critical for effectively capturing complex data characteristics. The metric learning of categorical data with hierarchical coupling relationships and local heterogeneous distributions is very challenging yet rarely explored. This paper proposes a Heterogeneous mEtric Learning with hIerarchical Couplings, HELIC, for this type of categorical data. HELIC captures both low-level value-to-attribute and high-level attribute-to-class hierarchical couplings, and reveals the intrinsic heterogeneities embedded in each level of couplings. Theoretical analyses of the effectiveness and generalization error bound verify that HELIC effectively represents the above complexities. Extensive experiments on 30 data sets with diverse characteristics demonstrate that HELIC-enabled classification significantly enhances the accuracy (up to 40.93%), compared with five state-of-the-art baselines.

show abstract

“…The result of classification in one-versus-one solution is the label of this class, which has been selected most frequently by the binary classifiers. Support vector method, although relatively young, has already found a number of practical applications, like [36][37][38][39][40][41].…”

Section: Machine Learning Using Knn Cart Chaid and Ann Classifiersmentioning

confidence: 99%

Comparative analysis of the properties of the nodular cast iron with carbides and the austempered ductile iron with use of the machine learning and the support vector machine

Wilk-Kołodziejczyk

Regulski

Gumienny

2016

Int J Adv Manuf Technol

View full text Add to dashboard Cite

The use of modern casting materials allows the achievement of higher product quality indices. The conducted experimental studies of new materials allow obtaining alloys with high performance properties while maintaining low production costs. Studies have shown that in certain areas of applications, the expensive to manufacture austempered ductile iron (ADI) can be replaced with ausferritic ductile iron or bainitic nodular cast iron with carbides, obtained without the heat treatment of castings. The dissemination of experimental results is possible through the use of information technologies and building applications that automatically compare the properties of materials, as the machine learning tools in comparative analysis of the properties of materials, in particular ADI and nodular cast iron with carbides.

show abstract

Improved support vector machine algorithm for heterogeneous data

Cited by 42 publications

References 27 publications

Methods of Computational Intelligence in the Context of Quality Assurance in Foundry Products

Methods of Computational Intelligence in the Context of Quality Assurance in Foundry Products

Heterogeneous Metric Learning of Categorical Data with Hierarchical Couplings

Comparative analysis of the properties of the nodular cast iron with carbides and the austempered ductile iron with use of the machine learning and the support vector machine

Contact Info

Product

Resources

About