Individual Classifier

14,000,000 Leading Edge Experts on the ideXlab platform

Scan Science and Technology

Contact Leading Edge Experts & Companies

Scan Science and Technology

Contact Leading Edge Experts & Companies

The Experts below are selected from a list of 20364 Experts worldwide ranked by ideXlab platform

Shane L Larson - One of the best experts on this subject based on the ideXlab platform.

  • gravity spy integrating advanced ligo detector characterization machine learning and citizen science
    Classical and Quantum Gravity, 2017
    Co-Authors: Michael Zevin, Sara Bahaadini, Emre Besler, Neda Rohani, Sarah Allen, M Cabero, Kevin Crowston, Aggelos K Katsaggelos, S B Coughlin, Shane L Larson
    Abstract:

    With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each Individual Classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.

  • gravity spy integrating advanced ligo detector characterization machine learning and citizen science
    arXiv: General Relativity and Quantum Cosmology, 2016
    Co-Authors: Michael Zevin, Scott Coughlin, Sara Bahaadini, Emre Besler, Neda Rohani, Sarah Allen, M Cabero, Kevin Crowston, Aggelos K Katsaggelos, Shane L Larson
    Abstract:

    (abridged for arXiv) With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of astronomy by providing an alternate means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each Individual Classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.

Fabio Roli - One of the best experts on this subject based on the ideXlab platform.

  • a theoretical analysis of bagging as a linear combination of Classifiers
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
    Co-Authors: Giorgio Fumera, Fabio Roli, Alessandra Serrau
    Abstract:

    We apply an analytical framework for the analysis of linearly combined Classifiers to ensembles generated by bagging. This provides an analytical model of bagging misclassification probability as a function of the ensemble size, which is a novel result in the literature. Experimental results on real data sets confirm the theoretical predictions. This allows us to derive a novel and theoretically grounded guideline for choosing bagging ensemble size. Furthermore, our results are consistent with explanations of bagging in terms of Classifier instability and variance reduction, support the optimality of the simple average over the weighted average combining rule for ensembles generated by bagging, and apply to other randomization-based methods for constructing Classifier ensembles. Although our results do not allow to compare bagging misclassification probability with the one of an Individual Classifier trained on the original training set, we discuss how the considered theoretical framework could be exploited to this aim.

  • a theoretical and experimental analysis of linear combiners for multiple Classifier systems
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005
    Co-Authors: Giorgio Fumera, Fabio Roli
    Abstract:

    In this paper, a theoretical and experimental analysis of linear combiners for multiple Classifier systems is presented. Although linear combiners are the most frequently used combining rules, many important issues related to their operation for pattern classification tasks lack a theoretical basis. After a critical review of the framework developed in works by Turner and Ghosh [1996], [1999] on which our analysis is based, we focus on the simplest and most widely used implementation of linear combiners, which consists of assigning a nonnegative weight to each Individual Classifier. Moreover, we consider the ideal performance of this combining rule, i.e., that achievable when the optimal values of the weights are used. We do not consider the problem of weights estimation, which has been addressed in the literature. Our theoretical analysis shows how the performance of linear combiners, in terms of misclassification probability, depends on the performance of Individual Classifiers, and on the correlation between their outputs. In particular, we evaluate the ideal performance improvement that can be achieved using the weighted average over the simple average combining rule and investigate in what way it depends on the Individual Classifiers. Experimental results on real data sets show that the behavior of linear combiners agrees with the predictions of our analytical model. Finally, we discuss the contribution to the state of the art and the practical relevance of our theoretical and experimental analysis of linear combiners for multiple Classifier systems.

Giorgio Fumera - One of the best experts on this subject based on the ideXlab platform.

  • a theoretical analysis of bagging as a linear combination of Classifiers
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
    Co-Authors: Giorgio Fumera, Fabio Roli, Alessandra Serrau
    Abstract:

    We apply an analytical framework for the analysis of linearly combined Classifiers to ensembles generated by bagging. This provides an analytical model of bagging misclassification probability as a function of the ensemble size, which is a novel result in the literature. Experimental results on real data sets confirm the theoretical predictions. This allows us to derive a novel and theoretically grounded guideline for choosing bagging ensemble size. Furthermore, our results are consistent with explanations of bagging in terms of Classifier instability and variance reduction, support the optimality of the simple average over the weighted average combining rule for ensembles generated by bagging, and apply to other randomization-based methods for constructing Classifier ensembles. Although our results do not allow to compare bagging misclassification probability with the one of an Individual Classifier trained on the original training set, we discuss how the considered theoretical framework could be exploited to this aim.

  • a theoretical and experimental analysis of linear combiners for multiple Classifier systems
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005
    Co-Authors: Giorgio Fumera, Fabio Roli
    Abstract:

    In this paper, a theoretical and experimental analysis of linear combiners for multiple Classifier systems is presented. Although linear combiners are the most frequently used combining rules, many important issues related to their operation for pattern classification tasks lack a theoretical basis. After a critical review of the framework developed in works by Turner and Ghosh [1996], [1999] on which our analysis is based, we focus on the simplest and most widely used implementation of linear combiners, which consists of assigning a nonnegative weight to each Individual Classifier. Moreover, we consider the ideal performance of this combining rule, i.e., that achievable when the optimal values of the weights are used. We do not consider the problem of weights estimation, which has been addressed in the literature. Our theoretical analysis shows how the performance of linear combiners, in terms of misclassification probability, depends on the performance of Individual Classifiers, and on the correlation between their outputs. In particular, we evaluate the ideal performance improvement that can be achieved using the weighted average over the simple average combining rule and investigate in what way it depends on the Individual Classifiers. Experimental results on real data sets show that the behavior of linear combiners agrees with the predictions of our analytical model. Finally, we discuss the contribution to the state of the art and the practical relevance of our theoretical and experimental analysis of linear combiners for multiple Classifier systems.

Andy C C Tan - One of the best experts on this subject based on the ideXlab platform.

  • multi agent decision fusion for motor fault diagnosis
    Mechanical Systems and Signal Processing, 2007
    Co-Authors: Gang Niu, Bo-suk Yang, Tian Han, Andy C C Tan
    Abstract:

    Improvement of recognition rate is ultimate aim for fault diagnosis researchers using pattern recognition techniques. However, the unique recognition method can only recognise a limited classification capability which is insufficient for real-life application. An ongoing strategy is the decision fusion techniques. In order to avoid the shortage of single information source coupled with unique decision method, a new approach is required to obtain better results. This paper proposes a decision fusion system for fault diagnosis, which integrates data sources from different types of sensors and decisions of multiple Classifiers. First, non-commensurate sensor data sets are combined using an improved sensor fusion method at a decision level by using relativity theory. The generated decision vectors are then selected based on correlation measure of Classifiers in order to find an optimal sequence of Classifiers fusion, which can lead to the best fusion performance. Finally, multi-agent Classifiers fusion algorithm is employed as the core of the whole fault diagnosis system. The efficiency of the proposed system was demonstrated through fault diagnosis of induction motors. The experimental results show that this system can lead to super performance when compared with the best Individual Classifier with single-source data.

Michael Zevin - One of the best experts on this subject based on the ideXlab platform.

  • gravity spy integrating advanced ligo detector characterization machine learning and citizen science
    Classical and Quantum Gravity, 2017
    Co-Authors: Michael Zevin, Sara Bahaadini, Emre Besler, Neda Rohani, Sarah Allen, M Cabero, Kevin Crowston, Aggelos K Katsaggelos, S B Coughlin, Shane L Larson
    Abstract:

    With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each Individual Classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.

  • gravity spy integrating advanced ligo detector characterization machine learning and citizen science
    arXiv: General Relativity and Quantum Cosmology, 2016
    Co-Authors: Michael Zevin, Scott Coughlin, Sara Bahaadini, Emre Besler, Neda Rohani, Sarah Allen, M Cabero, Kevin Crowston, Aggelos K Katsaggelos, Shane L Larson
    Abstract:

    (abridged for arXiv) With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of astronomy by providing an alternate means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each Individual Classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.