On the classification of dynamical data streams using novel “Anti-Bayesian” techniques.
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| Title: | On the classification of dynamical data streams using novel “Anti-Bayesian” techniques. |
|---|---|
| Authors: | Hammer, Hugo Lewi1, Yazidi, Anis1, Oommen, B. John2,3 |
| Source: | Pattern Recognition. Apr2018, Vol. 76, p108-124. 17p. |
| Subjects: | Dynamic data exchange, Bayesian analysis, Data mining, Quantiles, Robust statistics |
| Abstract: | The classification of dynamical data streams is among the most complex problems encountered in classification. This is, firstly, because the distribution of the data streams is non-stationary, and it changes without any prior “warning”. Secondly, the manner in which it changes is also unknown. Thirdly, and more interestingly, the model operates with the assumption that the correct classes of previously-classified patterns become available at a juncture after their appearance. This paper pioneers the use of unreported novel schemes that can classify such dynamical data streams by invoking the recently-introduced “Anti-Bayesian” (AB) techniques. Contrary to the Bayesian paradigm, that compare the testing sample with the distribution’s central points, AB techniques are based on the information in the distant-from-the-mean samples. Most Bayesian approaches can be naturally extended to dynamical systems by dynamically tracking the mean of each class using, for example, the exponential moving average based estimator, or a sliding window estimator. The AB schemes introduced by Oommen et al.., on the other hand, work with a radically different approach and with the non-central quantiles of the distributions. Surprisingly and counter-intuitively, the reported AB methods work equally or close-to-equally well to an optimal supervised Bayesian scheme on a host of accepted Pattern Recognition problems. This thus begs its natural extension to the unexplored arena of classification for dynamical data streams. Naturally, for such an AB classification approach, we need to track the non-stationarity of the quantiles of the classes. To achieve this, in this paper, we develop an AB approach for the online classification of data streams by applying the efficient and robust quantile estimators developed by Yazidi and Hammer [12,37]. Apart from the methodology itself, in this paper, we compare the Bayesian and AB approaches using both real-life and synthetic data. The results demonstrate the intriguing and counter-intuitive results that the AB approach, sometimes, actually outperforms the Bayesian approach for this application both with respect to the peak performance obtained, and the robustness of the choice of the respective tuning parameters. Furthermore, the AB approach is much more robust against outliers, which is an inherent property of quantile estimators [12,37], which is a property that the Bayesian approach cannot match, since it rather tracks the mean. [ABSTRACT FROM AUTHOR] |
| Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 127100118 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: On the classification of dynamical data streams using novel “Anti-Bayesian” techniques. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hammer%2C+Hugo+Lewi%22">Hammer, Hugo Lewi</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yazidi%2C+Anis%22">Yazidi, Anis</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Oommen%2C+B%2E+John%22">Oommen, B. John</searchLink><relatesTo>2,3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Pattern+Recognition%22">Pattern Recognition</searchLink>. Apr2018, Vol. 76, p108-124. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Dynamic+data+exchange%22">Dynamic data exchange</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Quantiles%22">Quantiles</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+statistics%22">Robust statistics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The classification of dynamical data streams is among the most complex problems encountered in classification. This is, firstly, because the distribution of the data streams is non-stationary, and it changes without any prior “warning”. Secondly, the manner in which it changes is also unknown. Thirdly, and more interestingly, the model operates with the assumption that the correct classes of previously-classified patterns become available at a juncture after their appearance. This paper pioneers the use of unreported novel schemes that can classify such dynamical data streams by invoking the recently-introduced “Anti-Bayesian” (AB) techniques. Contrary to the Bayesian paradigm, that compare the testing sample with the distribution’s central points, AB techniques are based on the information in the distant-from-the-mean samples. Most Bayesian approaches can be naturally extended to dynamical systems by dynamically tracking the mean of each class using, for example, the exponential moving average based estimator, or a sliding window estimator. The AB schemes introduced by Oommen et al.., on the other hand, work with a radically different approach and with the non-central quantiles of the distributions. Surprisingly and counter-intuitively, the reported AB methods work equally or close-to-equally well to an optimal supervised Bayesian scheme on a host of accepted Pattern Recognition problems. This thus begs its natural extension to the unexplored arena of classification for dynamical data streams. Naturally, for such an AB classification approach, we need to track the non-stationarity of the quantiles of the classes. To achieve this, in this paper, we develop an AB approach for the online classification of data streams by applying the efficient and robust quantile estimators developed by Yazidi and Hammer [12,37]. Apart from the methodology itself, in this paper, we compare the Bayesian and AB approaches using both real-life and synthetic data. The results demonstrate the intriguing and counter-intuitive results that the AB approach, sometimes, actually outperforms the Bayesian approach for this application both with respect to the peak performance obtained, and the robustness of the choice of the respective tuning parameters. Furthermore, the AB approach is much more robust against outliers, which is an inherent property of quantile estimators [12,37], which is a property that the Bayesian approach cannot match, since it rather tracks the mean. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.patcog.2017.10.031 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 108 Subjects: – SubjectFull: Dynamic data exchange Type: general – SubjectFull: Bayesian analysis Type: general – SubjectFull: Data mining Type: general – SubjectFull: Quantiles Type: general – SubjectFull: Robust statistics Type: general Titles: – TitleFull: On the classification of dynamical data streams using novel “Anti-Bayesian” techniques. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hammer, Hugo Lewi – PersonEntity: Name: NameFull: Yazidi, Anis – PersonEntity: Name: NameFull: Oommen, B. John IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 00313203 Numbering: – Type: volume Value: 76 Titles: – TitleFull: Pattern Recognition Type: main |
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