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Neurocomputing, ISSN 0925-2312, 07/2015, Volume 160, pp. 53 - 62
Machine learning algorithms are increasingly being applied in security-related tasks such as spam and malware detection, although their security properties... 
Support vector machines | Label flip attacks | Adversarial learning | Label noise | ROBUSTNESS | SECURITY | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Computer science | Algorithms | Data mining | Analysis | Machine learning
Journal Article
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 03/2017, Volume 39, Issue 3, pp. 486 - 500
Journal Article
IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, 10/2019, Volume 30, Issue 10, pp. 3172 - 3185
Journal Article
IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, 7/2019, pp. 1 - 1
For fault classification of chemical industries, the typical Fisher discriminant analysis (FDA) model requires that all the training samples should be... 
Training | ensemble learning | manifold-preserving sparse graph | discriminant analysis | industrial fault classification | label-noise | Process control | Robustness | Chemical processes | Labeling | Monitoring | Bagging
Journal Article
IEEE Transactions on Knowledge and Data Engineering, ISSN 1041-4347, 11/2019, Volume 31, Issue 11, pp. 2063 - 2078
The existing noise detection methods required the classifiers or distance measurements or data overall distribution, and `curse of dimensionality' and other... 
Training | Learning systems | Support vector machines | generalizability | Vegetation | Class noise | classifier | label noise | Noise measurement | Decision trees | k-means tree | BRANCH | QUALITY | SEARCH | ALGORITHM | COMPUTER SCIENCE, INFORMATION SYSTEMS | CLASSIFICATION | MODEL | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | GRAPHS | ENGINEERING, ELECTRICAL & ELECTRONIC | DECISION TREES | SETS
Journal Article
by Li, BC and Gao, Q
INTELLIGENT DATA ANALYSIS, ISSN 1088-467X, 2019, Volume 23, Issue 4, pp. 737 - 757
Data gathered from real world often contains label noise, which is harmful to the quality of data. Moreover, any data mining process suffers a deterioration... 
noise rate estimation | classification | noise correction | Label noise | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Data mining | Clustering | Noise | Machine learning
Journal Article
Expert Systems With Applications, ISSN 0957-4174, 10/2019, Volume 131, pp. 116 - 131
•A new semantic similarity based co-clustering kernel is proposed.•A mathematical proof for mercer's kernel is provided.•The algorithm embeds the task of... 
Semantic kernels | Support Vector Machines | Co-clustering | Label noise | Usage | Algorithms | Analysis | Machine learning | Similarity | Kernel functions | Noise | Clustering | Subspace methods | Support vector machines | Kernels | Training | Authoring | Digital media | Robustness (mathematics) | Outliers (statistics) | Classification
Journal Article
Knowledge-Based Systems, ISSN 0950-7051, 01/2018, Volume 140, pp. 27 - 49
•A novel class noise cleaner able not only to remove noisy instances but to successfully relabel them.•It introduces an improvement of the noise score measure... 
Data relabeling | Data preprocessing | Classification | Class noise | Noise filtering | Noisy data | Data reparation | KEEL | PERFORMANCE | LABEL NOISE | ALGORITHMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | SOFTWARE TOOL
Journal Article
Neurocomputing, ISSN 0925-2312, 07/2015, Volume 160, pp. 120 - 131
The advantage of ensemble methods over single methods is their ability to correct the errors of individual ensemble members and thereby improve the overall... 
Ensemble methods | Label noise | Diversity measures | Class noise | Noise detection | ATTRIBUTE NOISE | ASSOCIATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | CLASSIFIERS
Journal Article
Neurocomputing, ISSN 0925-2312, 07/2015, Volume 160, pp. 85 - 92
In this paper we investigate the usage of regularized correntropy framework for learning of classifiers from noisy labels. The class label predictors learned... 
Maximum Correntropy Criteria | Regularization | Pattern classification | Label noise | Maximum correntropy criteria | DISTRIBUTED GENERATION | NETWORK | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Data mining | Algorithms
Journal Article
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, 2018, Volume 10878, pp. 256 - 264
Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, 2018, Volume 11063, pp. 613 - 622
Conference Proceeding
Neurocomputing, ISSN 0925-2312, 07/2015, Volume 160, pp. 157 - 172
Learning with label noise is an important issue in classification, since it is not always possible to obtain reliable data labels. In this paper we explore and... 
High-dimensional data | Hubness | Label noise | Classification | K-nearest neighbor | Neighbor occurrence models | RULE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal Article
IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, 01/2019, Volume 30, Issue 1, pp. 163 - 174
Journal Article
Neural Processing Letters, ISSN 1370-4621, 10/2019, Volume 50, Issue 2, pp. 1845 - 1860
We present a simple but effective method for data cleaning and classification in the presence of label noise. The fundamental idea is to treat the data points... 
Outliers | Computational Intelligence | Artificial Intelligence | Complex Systems | Data cleaning | Computer Science | Classification | Class-specific autoencoder | Label noise
Journal Article
BMC GENOMICS, ISSN 1471-2164, 12/2019, Volume 20, Issue Suppl 9, pp. 913 - 10
Journal Article
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