MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation

2015 ◽  
Vol 89 ◽  
pp. 385-397 ◽  
Author(s):  
Francisco Charte ◽  
Antonio J. Rivera ◽  
María J. del Jesus ◽  
Francisco Herrera
Keyword(s):  
Multilabel Learning ◽  
Instance Generation

Adversarial Imbalance Classification with Class-Specific Diverse Instance Generation

2021 ◽  
Author(s):  
Qihang Shen ◽  
Xinyue Wang ◽  
Zixin Cai ◽  
Liping Jing
Keyword(s):  
Instance Generation

scholarly journals Hard instance generation for SAT

1997 ◽  
pp. 22-31 ◽  
Author(s):  
Satoshi Horie ◽  
Osamu Watanabe
Keyword(s):  
Instance Generation

An SMT-based Approach for Generating Coverage Oriented Metamodel Instances

2016 ◽  
Vol 7 (3) ◽  
pp. 23-50 ◽  
Author(s):  
Hao Wu
Keyword(s):  
General Level ◽  
Effective Technique ◽  
Coverage Criteria ◽  
Smt Solver ◽  
Instance Generation ◽  
Graph Property ◽  
Class Diagrams

An effective technique for generating instances of a metamodel should quickly and automatically generate instances satisfying the metamodel's structural and OCL constraints. Ideally it should also produce quantitatively meaningful instances with respect to certain criteria, that is, instances which meet specified generic coverage criteria that help the modelers test or verify a metamodel at a general level. In this paper, the author presents an approach consisting of two techniques for coverage oriented metamodel instance generation. The first technique realises the standard coverage criteria defined for UML class diagrams, while the second technique focuses on generating instances satisfying graph-based criteria. With the author's approach, both kinds of criteria are translated to SMT formulas which are then investigated by an SMT solver. Each successful assignment is then interpreted as a metamodel instance that provably satisfies a coverage criteria or a graph property. The author has already integrated this approach into his existing tool to demonstrate the feasibility.

scholarly journals Augmenting Multi-Instance Multilabel Learning with Sparse Bayesian Models for Skin Biopsy Image Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Gang Zhang ◽  
Jian Yin ◽  
Xiangyang Su ◽  
Yongjing Huang ◽  
Yingrong Lao ◽  
...  
Keyword(s):  
Skin Biopsy ◽  
Skin Diseases ◽  
Image Annotation ◽  
Texture Features ◽  
Skin Surface ◽  
Multilabel Learning ◽  
Machine Learning Model ◽  
Representation Method ◽  
Indirect Relationship ◽  
Biopsy Image

Skin biopsy images can reveal causes and severity of many skin diseases, which is a significant complement for skin surface inspection. Automatic annotation of skin biopsy image is an important problem for increasing efficiency and reducing the subjectiveness in diagnosis. However it is challenging particularly when there exists indirect relationship between annotation terms and local regions of a biopsy image, as well as local structures with different textures. In this paper, a novel method based on a recent proposed machine learning model, named multi-instance multilabel (MIML), is proposed to model the potential knowledge and experience of doctors on skin biopsy image annotation. We first show that the problem of skin biopsy image annotation can naturally be expressed as a MIML problem and then propose an image representation method that can capture both region structure and texture features, and a sparse Bayesian MIML algorithm which can produce probabilities indicating the confidence of annotation. The proposed algorithm framework is evaluated on a real clinical dataset containing 12,700 skin biopsy images. The results show that it is effective and prominent.

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