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Clustered K Nearest Neighbor Algorithm for Daily Inflow Forecasting

Mahmood Akbari, Peter Jules van Overloop, Abbas Afshar
2010 Water resources management  
As the selected attributes in the feature vector are determined overall on calibration data, there may be some data points whose outputs do not follow the considered attributes.  ...  In this sense, the similarity of a query instance is estimated according to the closeness of its feature vector with those of data available in calibration data.  ...  Acknowledgements The authors would like to thank Professor Nick van de Giesen, the head of Water Management section, Faculty CEG, Delft University of Technology.  ... 
doi:10.1007/s11269-010-9748-z fatcat:6o7b7qhh45gnpcoac6jos7q6i4

On the Suitability of Fuzzy Rule-Based Classification Systems with Noisy Data

J. Saez, J. Luengo, F. Herrera
2012 IEEE transactions on fuzzy systems  
Their evaluation on noisy test data and their combined use with noise filters are also shown to be appropriate.  ...  This paper analyzes the behavior of such systems with respect to classic crisp systems in the presence of noise.  ...  Sáez holds an FPU scholarship from the Spanish Ministry of Education and Science. J. Luengo holds a Post-Doctoral Research Fellowship at the University of Granada.  ... 
doi:10.1109/tfuzz.2012.2182774 fatcat:bjfnzgoxmrfzlekonakq5jnag4

Estimating attributes: Analysis and extensions of RELIEF [chapter]

Igor Kononenko
1994 Lecture Notes in Computer Science  
In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies among them.  ...  Original RELIEF can deal with discrete and continuous attributes and is limited to only two-class problems.  ...  Acknowledgements Part of this work was done during the author's stay at California Institute of Technology in Pasadena, CA. I would like to thank Padhraic Smyth and Prof.  ... 
doi:10.1007/3-540-57868-4_57 fatcat:6qo6pd44pbbw3kkrghbqxvc3uy

An Improved Naive Bayes Classifier-based Noise Detection Technique for Classifying User Phone Call Behavior [article]

Iqbal H. Sarker, Muhammad Ashad Kabir, Alan Colman, Jun Han
2017 arXiv   pre-print
probabilities of the attributes.  ...  We use this noise threshold to identify noisy instances.  ...  Hence, we summarize the effects of noisy instances for classifying user phone call behavior as follows: -Create unnecessary classification rules that are not interesting to the users and make the rule-set  ... 
arXiv:1710.04461v2 fatcat:4peyp32lknf25gwhqxyophs3ba

Effective classification of noisy data streams with attribute-oriented dynamic classifier selection

Xingquan Zhu, Xindong Wu, Ying Yang
2005 Knowledge and Information Systems  
Given a test instance, its attribute values determine the subsets that the similar instances in the evaluation set have constructed, and the classifier with the highest classification accuracy on those  ...  subsets is selected to classify the test instance.  ...  we use attribute values to determine the evaluation subsets for each test instance.  ... 
doi:10.1007/s10115-005-0212-y fatcat:ndlcau4kqzgx5bjuxqud2fjle4

Exponential Loss Minimization for Learning Weighted Naive Bayes Classifiers

Taeheung Kim, Jong-Seok Lee
2022 IEEE Access  
allows for the simple modification of the loss function such that the naive Bayes classifier becomes robust to noisy instances.  ...  This research begins with a typical exponential loss which is sensitive to noise and provides a series of its modifications to make naive Bayes classifiers more robust to noisy instances.  ...  Therefore, we can expect that L dev is less sensitive than L exp to noisy instances, examples of which are instances that are difficult to classify.  ... 
doi:10.1109/access.2022.3155231 fatcat:kmt65oaljjbrdockthsgowygwm

Pareto Inspired Multi-objective Rule Fitness for Noise-Adaptive Rule-Based Machine Learning [chapter]

Ryan J. Urbanowicz, Randal S. Olson, Jason H. Moore
2016 Lecture Notes in Computer Science  
The fitness of individual LCS rules is commonly based on accuracy, but this metric alone is not ideal for assessing global rule 'value' in noisy problem domains and thus impedes effective knowledge extraction  ...  Multi-objective fitness functions are promising but rely on prior knowledge of how to weigh objective importance (typically unavailable in real world problems).  ...  One interesting trend is that in partially noisy problems, CoverMax tends to be not only large, but larger than AccuracyMax.  ... 
doi:10.1007/978-3-319-45823-6_48 fatcat:4tiriqzeqfcxvcv7eiiawispvi

A Machine Learning based Robust Prediction Model for Real-life Mobile Phone Data [article]

Iqbal H. Sarker
2019 arXiv   pre-print
In our robust model, we first effectively identify and eliminate the noisy instances from the training dataset by determining a dynamic noise threshold using naive Bayes classifier and laplace estimator  ...  Real-life mobile phone data may contain noisy instances, which is a fundamental issue for building a prediction model with many potential negative consequences.  ...  Acknowledgment The author would like to thank Prof. Jun Han, Swinburne University of Technology, Australia, Dr. Alan Colman, Swinburne University of Technology, Australia, and Dr.  ... 
arXiv:1902.07588v1 fatcat:oylakibpcnad7brktskheytcye

Class Noise vs. Attribute Noise: A Quantitative Study

Xingquan Zhu, Xindong Wu
2004 Artificial Intelligence Review  
A more reasonable solution might be to employ some preprocessing mechanisms to handle noisy instances before a learner is formed.  ...  Our conclusions can be used to guide interested readers to enhance data quality by designing various noise handling mechanisms.  ...  noisy instances.  ... 
doi:10.1007/s10462-004-0751-8 fatcat:vijgozyga5d65ntaxdgdke233u

Automated breast cancer classification using near-infrared optical tomographic images

James Z. Wang, Xiaoping Liang, Qizhi Zhang, Laurie L. Fajardo, Huabei Jiang
2008 Journal of Biomedical Optics  
A support vector machine ͑SVM͒ classifier is utilized to distinguish the malignant from the benign lesions using the automatically extracted attributes.  ...  The classification results of in vivo tomographic images from 35 breast masses using absorption, scattering, and refractive index attributes demonstrate high sensitivity, specificity, and overall accuracy  ...  Acknowledgments This work was supported in part by a grant from the National Institutes of Health ͑NIH͒, Grant No. R01 CA090533.  ... 
doi:10.1117/1.2956662 pmid:19021329 fatcat:2kn6zs72irbzfarnv2ur4as2jy

Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets

Min-Wei Huang, Wei-Chao Lin, Chih-Fong Tsai
2018 Journal of Healthcare Engineering  
Many real-world medical datasets contain some proportion of missing (attribute) values.  ...  The aim of this paper is to examine whether a combination of instance selection from the observed data and missing value imputation offers better performance than performing missing value imputation alone  ...  Acknowledgments This research is partially supported by the Ministry of Science and Technology of Taiwan (MOST 103-2410-H-008-034-MY2).  ... 
doi:10.1155/2018/1817479 pmid:29599943 pmcid:PMC5823414 fatcat:nh5gzpxgxbf25gwuhkyegzyemi

Rapid Rule Compaction Strategies for Global Knowledge Discovery in a Supervised Learning Classifier System

Jie Tan, Jason Moore, Ryan Urbanowicz
2013 Advances in Artificial Life, ECAL 2013  
However, existing rule compaction strategies tend to reduce overall rule population performance along with population size, especially in the context of noisy problem domains such as bioinformatics.  ...  The resulting 'model' learned by these algorithms is comprised of an entire population of rules, some of which will inevitably be redundant or poor predictors.  ...  Specifically we are most interested in applying rule compaction to problems domains with complex and noisy patterns.  ... 
doi:10.7551/978-0-262-31709-2-ch017 dblp:conf/ecal/TanMU13 fatcat:74lfhysnjzblln7gcwzoxgcl4a

Mining With Noise Knowledge: Error-Aware Data Mining

Xindong Wu, Xingquan Zhu
2008 IEEE transactions on systems, man and cybernetics. Part A. Systems and humans  
Two common practices are to adopt either data cleansing approaches to enhance the data consistency or simply take noisy data as quality sources and feed them into the data mining algorithms.  ...  In this paper, we consider an error-aware (EA) data mining design, which takes advantage of statistical error information (such as noise level and noise distribution) to improve data mining results.  ...  ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers whose constructive comments and advice helped improve this paper.  ... 
doi:10.1109/tsmca.2008.923034 fatcat:dfqwtsiaifhl7h4innceb27ldu

Psycho-Visual Quality Assessment Of State-Of-The-Art Denoising Schemes

Ewout Vansteenkiste
2006 Zenodo  
Publication in the conference proceedings of EUSIPCO, Florence, Italy, 2006  ...  Especially, we would like to thank all authors taking part in this experiment.  ...  Stefaan Lippens for the creation of the web-interface on which the psycho-visual experiment was performed http://telin.ugent.be/˜slippens/exp/ (in dutch).  ... 
doi:10.5281/zenodo.53014 fatcat:gppqv7lfkjazbl6vxaqw4hozty

Application-Independent Feature Construction from Noisy Samples [chapter]

Dominique Gay, Nazha Selmaoui, Jean-François Boulicaut
2009 Lecture Notes in Computer Science  
Our experiments on noisy training data shows accuracy improvement when using the computed features instead of the original ones.  ...  In this paper, we focus on "non-class" attribute noise and we consider how a frequent fault-tolerant (FFT) pattern mining task can be used to support noise-tolerant classification.  ...  Instead of removing noisy instances or correcting noisy values, we propose a method to cope with attribute noise without changing or removing any attributes values in the training data.  ... 
doi:10.1007/978-3-642-01307-2_102 fatcat:fluy4kq6qvffvhvzsfruegugsu
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