The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform

Laurent Heutte - One of the best experts on this subject based on the ideXlab platform.

  • One class random forests
    Pattern Recognition, 2013
    Co-Authors: Chesner Désir, Simon Bernard, Caroline Petitjean, Laurent Heutte
    Abstract:

    One class Classification is a Binary Classification Task for which only one class of samples is available for learning. In some preliminary works, we have proposed One Class Random Forests (OCRF), a method based on a random forest algorithm and an original outlier generation procedure that makes use of classifier ensemble randomization principles. In this paper, we propose an extensive study of the behavior of OCRF, that includes experiments on various UCI public datasets and comparison to reference one class namely, Gaussian density models, Parzen estimators, Gaussian mixture models and One Class SVMs-with statistical significance. Our aim is to show that the randomization principles embedded in a random forest algorithm make the outlier generation process more efficient, and allow in particular to break the curse of dimensionality. One Class Random Forests are shown to perform well in comparison to other methods, and in particular to maintain stable performance in higher dimension, while the other algorithms may fail.

Robert Burduk - One of the best experts on this subject based on the ideXlab platform.

  • classifier selection uses decision profiles in Binary Classification Task
    IP&C, 2016
    Co-Authors: Paulina Baczynska, Robert Burduk
    Abstract:

    The dynamic selection of classifiers plays an important role in the creation of an ensemble of classifiers. The paper presents the dynamic selection of a posteriori probability function based on the analysis of the decision profiles. The idea of the dynamic selection is exemplified with the Binary Classification Task. In addition, a number of experiments have been carried out on ten benchmark data sets.

  • CORES - Discriminant Function Selection in Binary Classification Task
    Advances in Intelligent Systems and Computing, 2016
    Co-Authors: Robert Burduk
    Abstract:

    The ensemble selection is one of the important problems in building multiple classifier systems (MCSs). This paper presents dynamic ensemble selection based on the analysis of discriminant functions. The idea of the selection is presented on the basis of Binary Classification Tasks. The paper presents two approaches: one takes into account the normalization of the discrimination functions, and in the second approach, normalization is not performed. The reported results based on the data sets form the UCI repository show that the proposed ensemble selection is a promising method for the development of MCSs.

  • discriminant function selection in Binary Classification Task
    Computer Recognition Systems, 2016
    Co-Authors: Robert Burduk
    Abstract:

    The ensemble selection is one of the important problems in building multiple classifier systems (MCSs). This paper presents dynamic ensemble selection based on the analysis of discriminant functions. The idea of the selection is presented on the basis of Binary Classification Tasks. The paper presents two approaches: one takes into account the normalization of the discrimination functions, and in the second approach, normalization is not performed. The reported results based on the data sets form the UCI repository show that the proposed ensemble selection is a promising method for the development of MCSs.

  • IDEAL - Ensemble Selection Based on Discriminant Functions in Binary Classification Task
    Intelligent Data Engineering and Automated Learning – IDEAL 2015, 2015
    Co-Authors: Paulina Baczyńska, Robert Burduk
    Abstract:

    The paper describes the dynamic ensemble selection. The proposed algorithm uses values of the discriminant functions and it is dedicated to the Binary Classification Task. The proposed algorithm of the ensemble selection uses decision profiles and the normalization of the discrimination functions is carried out. Additionally, the difference of the discriminant functions is used as one condition of selection. The reported results based on the ten data sets from the UCI repository show that the proposed dynamic ensemble selection is a promising method for the development of multiple classifiers systems.

  • ensemble selection based on discriminant functions in Binary Classification Task
    Intelligent Data Engineering and Automated Learning, 2015
    Co-Authors: Paulina Baczynska, Robert Burduk
    Abstract:

    The paper describes the dynamic ensemble selection. The proposed algorithm uses values of the discriminant functions and it is dedicated to the Binary Classification Task. The proposed algorithm of the ensemble selection uses decision profiles and the normalization of the discrimination functions is carried out. Additionally, the difference of the discriminant functions is used as one condition of selection. The reported results based on the ten data sets from the UCI repository show that the proposed dynamic ensemble selection is a promising method for the development of multiple classifiers systems.

Zhanpeng Shao - One of the best experts on this subject based on the ideXlab platform.

  • ICARCV - Online visual object tracking with supervised sparse representation and learning
    2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), 2014
    Co-Authors: Tianxiang Bai, Zhanpeng Shao
    Abstract:

    In this paper, an online visual object tracking algorithm based on the discriminative sparse representation framework with supervised learning is proposed. Different from the generative sparse representation based tracking algorithms, the proposed method casts the tracking problem into a Binary Classification Task. A linear classifier is embedded into the sparse representation model by incorporating the Classification error into the objective function to achieve discriminative Classification. The dictionary and the classifier are jointly trained using the online dictionary learning algorithm, thus allow the model can adapt the dynamic variations of target appearance and background environment. The target locations are updated based on the Classification score and the greedy search motion model. The proposed method is evaluated using four benchmark datasets and is compared with three state-of-the-art tracking algorithms. The results show that the discriminative sparse representation facilitates the tracking performance.

Tapio Salakoski - One of the best experts on this subject based on the ideXlab platform.

  • utu at semeval 2016 Task 10 Binary Classification for expression detection bced
    North American Chapter of the Association for Computational Linguistics, 2016
    Co-Authors: Jari Bjorne, Tapio Salakoski
    Abstract:

    The SemEval 2016 DiMSUM Shared Task concerns the detection of minimal semantic units from text and prediction of their coarse lexical categories known as supersenses. Our approach is to define this Task as a Binary Classification problem approachable by straightforward machine learning methods. We start by detecting semantic units by matching text spans against several large dictionaries, including the English WordNet, expressions derived from the Yelp Academic Dataset and concepts from the English Wikipedia, generating a set of potential supersenses for each matched span. For each potential supersense and text span pair a Binary machine learning example is defined. We classify these examples using an ensemble method, taking as the final predicted supersense the one with the highest confidence score. Our system achieves good performance on the supersense Classification Task but has limited performance for detection of multi-word semantic units. We show that the Task of supersense prediction can be effectively defined as a Binary Classification Task.

Kar-ann Toh - One of the best experts on this subject based on the ideXlab platform.

  • ICICS - Visual tracking with online discriminative learning
    2011 8th International Conference on Information Communications & Signal Processing, 2011
    Co-Authors: Se-in Jang, Kwontaeg Choi, Youngsung Kim, Kar-ann Toh
    Abstract:

    We treat tracking as a Binary Classification Task in order to distinguish between an object to be tracked and the background. We propose to integrate an online learning based total-error-rate minimization method (OTER) with an observation model of particle filter for visual tracking. The particle filter is modeled using an affine dynamic model and an observation model. The observation model is constructed using the OTER classifier for Binary pattern Classification. The proposed method is empirically evaluated both qualitatively and quantitatively using several publicly available video sequences.