The Experts below are selected from a list of 279 Experts worldwide ranked by ideXlab platform

Nada Lavrač - One of the best experts on this subject based on the ideXlab platform.

  • Ensemble-based Noise detection: Noise ranking and visual performance evaluation
    Data Mining and Knowledge Discovery, 2014
    Co-Authors: Borut Sluban, Dragan Gamberger, Nada Lavrač
    Abstract:

    Noise filtering is most frequently used in data preprocessing to improve the accuracy of induced classifiers. The focus of this work is different: we aim at detecting noisy instances for improved data understanding, data cleaning and outlier identification. The paper is composed of three parts. The first part presents an ensemble-based Noise ranking methodology for explicit Noise and outlier identification, named Noise- Rank , which was successfully applied to a real-life medical problem as proven in domain expert evaluation. The second part is concerned with quantitative performance evaluation of Noise detection algorithms on data with randomly Injected Noise. A methodology for visual performance evaluation of Noise detection algorithms in the precision-recall space, named Viper , is presented and compared to standard evaluation practice. The third part presents the implementation of the NoiseRank and Viper methodologies in a web-based platform for composition and execution of data mining workflows. This implementation allows public accessibility of the developed approaches, repeatability and sharing of the presented experiments as well as the inclusion of web services enabling to incorporate new Noise detection algorithms into the proposed Noise detection and performance evaluation workflows.

  • Ensemble-based Noise detection: Noise ranking and visual performance evaluation
    Data Mining and Knowledge Discovery, 2014
    Co-Authors: Borut Sluban, Dragan Gamberger, Nada Lavrač
    Abstract:

    Noise filtering is most frequently used in data preprocessing to improve the accuracy of induced classifiers. The focus of this work is different: we aim at detecting noisy instances for improved data understanding, data cleaning and outlier identification. The paper is composed of three parts. The first part presents an ensemble-based Noise ranking methodology for explicit Noise and outlier identification, named Noise- Rank, which was successfully applied to a real-life medical problem as proven in domain expert evaluation. The second part is concerned with quantitative performance evaluation of Noise detection algorithms on data with randomly Injected Noise. A methodology for visual performance evaluation of Noise detection algorithms in the precision-recall space, named Viper, is presented and compared to standard evaluation practice. The third part presents the implementation of the NoiseRank and Viper methodologies in a web-based platform for composition and execution of data mining workflows. This implementation allows public accessibility of the developed approaches, repeatability and sharing of the presented experiments as well as the inclusion of web services enabling to incorporate new Noise detection algorithms into the proposed Noise detection and performance evaluation workflows. © 2012 The Author(s).

Borut Sluban - One of the best experts on this subject based on the ideXlab platform.

  • Ensemble-based Noise detection: Noise ranking and visual performance evaluation
    Data Mining and Knowledge Discovery, 2014
    Co-Authors: Borut Sluban, Dragan Gamberger, Nada Lavrač
    Abstract:

    Noise filtering is most frequently used in data preprocessing to improve the accuracy of induced classifiers. The focus of this work is different: we aim at detecting noisy instances for improved data understanding, data cleaning and outlier identification. The paper is composed of three parts. The first part presents an ensemble-based Noise ranking methodology for explicit Noise and outlier identification, named Noise- Rank , which was successfully applied to a real-life medical problem as proven in domain expert evaluation. The second part is concerned with quantitative performance evaluation of Noise detection algorithms on data with randomly Injected Noise. A methodology for visual performance evaluation of Noise detection algorithms in the precision-recall space, named Viper , is presented and compared to standard evaluation practice. The third part presents the implementation of the NoiseRank and Viper methodologies in a web-based platform for composition and execution of data mining workflows. This implementation allows public accessibility of the developed approaches, repeatability and sharing of the presented experiments as well as the inclusion of web services enabling to incorporate new Noise detection algorithms into the proposed Noise detection and performance evaluation workflows.

  • Ensemble-based Noise detection: Noise ranking and visual performance evaluation
    Data Mining and Knowledge Discovery, 2014
    Co-Authors: Borut Sluban, Dragan Gamberger, Nada Lavrač
    Abstract:

    Noise filtering is most frequently used in data preprocessing to improve the accuracy of induced classifiers. The focus of this work is different: we aim at detecting noisy instances for improved data understanding, data cleaning and outlier identification. The paper is composed of three parts. The first part presents an ensemble-based Noise ranking methodology for explicit Noise and outlier identification, named Noise- Rank, which was successfully applied to a real-life medical problem as proven in domain expert evaluation. The second part is concerned with quantitative performance evaluation of Noise detection algorithms on data with randomly Injected Noise. A methodology for visual performance evaluation of Noise detection algorithms in the precision-recall space, named Viper, is presented and compared to standard evaluation practice. The third part presents the implementation of the NoiseRank and Viper methodologies in a web-based platform for composition and execution of data mining workflows. This implementation allows public accessibility of the developed approaches, repeatability and sharing of the presented experiments as well as the inclusion of web services enabling to incorporate new Noise detection algorithms into the proposed Noise detection and performance evaluation workflows. © 2012 The Author(s).

Jeanpierre Raskin - One of the best experts on this subject based on the ideXlab platform.

  • rf performance of soi cmos technology on commercial 200 mm enhanced signal integrity high resistivity soi substrate
    IEEE Transactions on Electron Devices, 2014
    Co-Authors: Khaled Ben Ali, Cesar Roda Neve, Ali Gharsallah, Jeanpierre Raskin
    Abstract:

    RF performance of a 200-mm commercial-enhanced signal integrity high resistivity silicon-on-insulator (eSI HR-SOI) substrate is investigated and compared with its counterpart HR-SOI wafer. By measuring coplanar waveguide lines and substrate crosstalk structures, it is demonstrated that losses are completely suppressed leading to virtually lossless linear substrate. Moreover, a reduction of the second harmonic distortion by more than 25 dB is measured on eSI HR-SOI wafer compared with HR-SOI. Excellent matching between experimental dc and RF characteristics of fully depleted SOI MOSFETs measured on top of HR-SOI and eSI HR-SOI is demonstrated. Furthermore, digital substrate Noise is reduced by more than 25 dB on eSI HR-SOI compared with HR-SOI, when Injected Noise varies from 500 kHz to 50 MHz. The eSI HR-SOI substrate is fully compatible with the CMOS process and could be considered as a promising solution for the RF front-end-modules integration and system-on-chip applications.

Dragan Gamberger - One of the best experts on this subject based on the ideXlab platform.

  • Ensemble-based Noise detection: Noise ranking and visual performance evaluation
    Data Mining and Knowledge Discovery, 2014
    Co-Authors: Borut Sluban, Dragan Gamberger, Nada Lavrač
    Abstract:

    Noise filtering is most frequently used in data preprocessing to improve the accuracy of induced classifiers. The focus of this work is different: we aim at detecting noisy instances for improved data understanding, data cleaning and outlier identification. The paper is composed of three parts. The first part presents an ensemble-based Noise ranking methodology for explicit Noise and outlier identification, named Noise- Rank , which was successfully applied to a real-life medical problem as proven in domain expert evaluation. The second part is concerned with quantitative performance evaluation of Noise detection algorithms on data with randomly Injected Noise. A methodology for visual performance evaluation of Noise detection algorithms in the precision-recall space, named Viper , is presented and compared to standard evaluation practice. The third part presents the implementation of the NoiseRank and Viper methodologies in a web-based platform for composition and execution of data mining workflows. This implementation allows public accessibility of the developed approaches, repeatability and sharing of the presented experiments as well as the inclusion of web services enabling to incorporate new Noise detection algorithms into the proposed Noise detection and performance evaluation workflows.

  • Ensemble-based Noise detection: Noise ranking and visual performance evaluation
    Data Mining and Knowledge Discovery, 2014
    Co-Authors: Borut Sluban, Dragan Gamberger, Nada Lavrač
    Abstract:

    Noise filtering is most frequently used in data preprocessing to improve the accuracy of induced classifiers. The focus of this work is different: we aim at detecting noisy instances for improved data understanding, data cleaning and outlier identification. The paper is composed of three parts. The first part presents an ensemble-based Noise ranking methodology for explicit Noise and outlier identification, named Noise- Rank, which was successfully applied to a real-life medical problem as proven in domain expert evaluation. The second part is concerned with quantitative performance evaluation of Noise detection algorithms on data with randomly Injected Noise. A methodology for visual performance evaluation of Noise detection algorithms in the precision-recall space, named Viper, is presented and compared to standard evaluation practice. The third part presents the implementation of the NoiseRank and Viper methodologies in a web-based platform for composition and execution of data mining workflows. This implementation allows public accessibility of the developed approaches, repeatability and sharing of the presented experiments as well as the inclusion of web services enabling to incorporate new Noise detection algorithms into the proposed Noise detection and performance evaluation workflows. © 2012 The Author(s).

Alexandre M. Bayen - One of the best experts on this subject based on the ideXlab platform.

  • simulation to scaled city zero shot policy transfer for traffic control via autonomous vehicles
    International Conference on Cyber-Physical Systems, 2019
    Co-Authors: Kathy Jang, Eugene Vinitsky, Behdad Chalaki, Ben Remer, Logan E. Beaver, Andreas A. Malikopoulos, Alexandre M. Bayen
    Abstract:

    Using deep reinforcement learning, we successfully train a set of two autonomous vehicles to lead a fleet of vehicles onto a round-about and then transfer this policy from simulation to a scaled city without fine-tuning. We use Flow, a library for deep reinforcement learning in microsimulators, to train two policies, (1) a policy with Noise Injected into the state and action space and (2) a policy without any Injected Noise. In simulation, the autonomous vehicles learn an emergent metering behavior for both policies which allows smooth merging. We then directly transfer this policy without any tuning to the University of Delaware's Scaled Smart City (UDSSC), a 1:25 scale testbed for connected and automated vehicles. We characterize the performance of the transferred policy based on how thoroughly the ramp metering behavior is captured in UDSSC. We show that the Noise-free policy results in severe slowdowns and only, occasionally, it exhibits acceptable metering behavior. On the other hand, the Noise-Injected policy consistently performs an acceptable metering behavior, implying that the Noise eventually aids with the zero-shot policy transfer. Finally, the transferred, Noise-Injected policy leads to a 5% reduction of average travel time and a reduction of 22% in maximum travel time in the UDSSC. Videos of the proposed self-learning controllers can be found at https://sites.google.com/view/iccps-policy-transfer.

  • ICCPS - Simulation to scaled city: zero-shot policy transfer for traffic control via autonomous vehicles
    Proceedings of the 10th ACM IEEE International Conference on Cyber-Physical Systems, 2019
    Co-Authors: Kathy Jang, Eugene Vinitsky, Behdad Chalaki, Ben Remer, Logan E. Beaver, Andreas A. Malikopoulos, Alexandre M. Bayen
    Abstract:

    Using deep reinforcement learning, we successfully train a set of two autonomous vehicles to lead a fleet of vehicles onto a round-about and then transfer this policy from simulation to a scaled city without fine-tuning. We use Flow, a library for deep reinforcement learning in microsimulators, to train two policies, (1) a policy with Noise Injected into the state and action space and (2) a policy without any Injected Noise. In simulation, the autonomous vehicles learn an emergent metering behavior for both policies which allows smooth merging. We then directly transfer this policy without any tuning to the University of Delaware's Scaled Smart City (UDSSC), a 1:25 scale testbed for connected and automated vehicles. We characterize the performance of the transferred policy based on how thoroughly the ramp metering behavior is captured in UDSSC. We show that the Noise-free policy results in severe slowdowns and only, occasionally, it exhibits acceptable metering behavior. On the other hand, the Noise-Injected policy consistently performs an acceptable metering behavior, implying that the Noise eventually aids with the zero-shot policy transfer. Finally, the transferred, Noise-Injected policy leads to a 5% reduction of average travel time and a reduction of 22% in maximum travel time in the UDSSC. Videos of the proposed self-learning controllers can be found at https://sites.google.com/view/iccps-policy-transfer.

  • Simulation to scaled city: zero-shot policy transfer for traffic control via autonomous vehicles
    arXiv: Systems and Control, 2018
    Co-Authors: Kathy Jang, Eugene Vinitsky, Behdad Chalaki, Ben Remer, Logan E. Beaver, Andreas A. Malikopoulos, Alexandre M. Bayen
    Abstract:

    Using deep reinforcement learning, we train control policies for autonomous vehicles leading a platoon of vehicles onto a roundabout. Using Flow, a library for deep reinforcement learning in micro-simulators, we train two policies, one policy with Noise Injected into the state and action space and one without any Injected Noise. In simulation, the autonomous vehicle learns an emergent metering behavior for both policies in which it slows to allow for smoother merging. We then directly transfer this policy without any tuning to the University of Delaware Scaled Smart City (UDSSC), a 1:25 scale testbed for connected and automated vehicles. We characterize the performance of both policies on the scaled city. We show that the Noise-free policy winds up crashing and only occasionally metering. However, the Noise-Injected policy consistently performs the metering behavior and remains collision-free, suggesting that the Noise helps with the zero-shot policy transfer. Additionally, the transferred, Noise-Injected policy leads to a 5% reduction of average travel time and a reduction of 22% in maximum travel time in the UDSSC. Videos of the controllers can be found at this https URL.