The Experts below are selected from a list of 9567 Experts worldwide ranked by ideXlab platform

Edsel A Pena - One of the best experts on this subject based on the ideXlab platform.

  • multinomial probit bayesian additive regression trees
    Stat (International Statistical Institute), 2016
    Co-Authors: Bereket P Kindo, Hao Wang, Edsel A Pena
    Abstract:

    This article proposes multinomial probit Bayesian additive regression trees (MPBART) as a multinomial probit extension of BART - Bayesian additive regression trees. MPBART is flexible to allow inclusion of predictors that describe the observed units as well as the available choice alternatives. Through two simulation studies and four real data examples, we show that MPBART exhibits very good predictive performance in comparison to other discrete choice and Multiclass Classification methods. To implement MPBART, the R package mpbart is freely available from CRAN repositories.

  • mpbart multinomial probit bayesian additive regression trees
    arXiv: Machine Learning, 2013
    Co-Authors: Bereket P Kindo, Hao Wang, Edsel A Pena
    Abstract:

    This article proposes Multinomial Probit Bayesian Additive Regression Trees (MPBART) as a multinomial probit extension of BART - Bayesian Additive Regression Trees (Chipman et al (2010)). MPBART is flexible to allow inclusion of predictors that describe the observed units as well as the available choice alternatives. Through two simulation studies and four real data examples, we show that MPBART exhibits very good predictive performance in comparison to other discrete choice and Multiclass Classification methods. To implement MPBART, we have developed an R package mpbart available freely from CRAN repositories.

Bereket P Kindo - One of the best experts on this subject based on the ideXlab platform.

  • multinomial probit bayesian additive regression trees
    Stat (International Statistical Institute), 2016
    Co-Authors: Bereket P Kindo, Hao Wang, Edsel A Pena
    Abstract:

    This article proposes multinomial probit Bayesian additive regression trees (MPBART) as a multinomial probit extension of BART - Bayesian additive regression trees. MPBART is flexible to allow inclusion of predictors that describe the observed units as well as the available choice alternatives. Through two simulation studies and four real data examples, we show that MPBART exhibits very good predictive performance in comparison to other discrete choice and Multiclass Classification methods. To implement MPBART, the R package mpbart is freely available from CRAN repositories.

  • mpbart multinomial probit bayesian additive regression trees
    arXiv: Machine Learning, 2013
    Co-Authors: Bereket P Kindo, Hao Wang, Edsel A Pena
    Abstract:

    This article proposes Multinomial Probit Bayesian Additive Regression Trees (MPBART) as a multinomial probit extension of BART - Bayesian Additive Regression Trees (Chipman et al (2010)). MPBART is flexible to allow inclusion of predictors that describe the observed units as well as the available choice alternatives. Through two simulation studies and four real data examples, we show that MPBART exhibits very good predictive performance in comparison to other discrete choice and Multiclass Classification methods. To implement MPBART, we have developed an R package mpbart available freely from CRAN repositories.

Hao Wang - One of the best experts on this subject based on the ideXlab platform.

  • multinomial probit bayesian additive regression trees
    Stat (International Statistical Institute), 2016
    Co-Authors: Bereket P Kindo, Hao Wang, Edsel A Pena
    Abstract:

    This article proposes multinomial probit Bayesian additive regression trees (MPBART) as a multinomial probit extension of BART - Bayesian additive regression trees. MPBART is flexible to allow inclusion of predictors that describe the observed units as well as the available choice alternatives. Through two simulation studies and four real data examples, we show that MPBART exhibits very good predictive performance in comparison to other discrete choice and Multiclass Classification methods. To implement MPBART, the R package mpbart is freely available from CRAN repositories.

  • mpbart multinomial probit bayesian additive regression trees
    arXiv: Machine Learning, 2013
    Co-Authors: Bereket P Kindo, Hao Wang, Edsel A Pena
    Abstract:

    This article proposes Multinomial Probit Bayesian Additive Regression Trees (MPBART) as a multinomial probit extension of BART - Bayesian Additive Regression Trees (Chipman et al (2010)). MPBART is flexible to allow inclusion of predictors that describe the observed units as well as the available choice alternatives. Through two simulation studies and four real data examples, we show that MPBART exhibits very good predictive performance in comparison to other discrete choice and Multiclass Classification methods. To implement MPBART, we have developed an R package mpbart available freely from CRAN repositories.

Takafumi Kanamori - One of the best experts on this subject based on the ideXlab platform.

  • deformation of log likelihood loss function for Multiclass boosting
    Neural Networks, 2010
    Co-Authors: Takafumi Kanamori
    Abstract:

    The purpose of this paper is to study loss functions in Multiclass Classification. In Classification problems, the decision function is estimated by minimizing an empirical loss function, and then, the output label is predicted by using the estimated decision function. We propose a class of loss functions which is obtained by a deformation of the log-likelihood loss function. There are four main reasons why we focus on the deformed log-likelihood loss function: (1) this is a class of loss functions which has not been deeply investigated so far, (2) in terms of computation, a boosting algorithm with a pseudo-loss is available to minimize the proposed loss function, (3) the proposed loss functions provide a clear correspondence between the decision functions and conditional probabilities of output labels, (4) the proposed loss functions satisfy the statistical consistency of the Classification error rate which is a desirable property in Classification problems. Based on (3), we show that the deformed log-likelihood loss provides a model of mislabeling which is useful as a statistical model of medical diagnostics. We also propose a robust loss function against outliers in Multiclass Classification based on our approach. The robust loss function is a natural extension of the existing robust loss function for binary Classification. A model of mislabeling and a robust loss function are useful to cope with noisy data. Some numerical studies are presented to show the robustness of the proposed loss function. A mathematical characterization of the deformed log-likelihood loss function is also presented.

  • Multiclass boosting algorithms for shrinkage estimators of class probability
    Algorithmic Learning Theory, 2007
    Co-Authors: Takafumi Kanamori
    Abstract:

    Our purpose is to estimate conditional probabilities of output labels in Multiclass Classification problems. Adaboost provides highly accurate classifiers and has potential to estimate conditional probabilities. However, the conditional probability estimated by Adaboost tends to overfit to training samples. We propose loss functions for boosting that provide shrinkage estimator. The effect of regularization is realized by shrinkage of probabilities toward the uniform distribution. Numerical experiments indicate that boosting algorithms based on proposed loss functions show significantly better results than existing boosting algorithms for estimation of conditional probabilities.

Liangbiao Chen - One of the best experts on this subject based on the ideXlab platform.

  • molecular Classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines
    FEBS Letters, 2003
    Co-Authors: Sihua Peng, Xuefeng B Ling, Xiaoning Peng, Liangbiao Chen
    Abstract:

    Simultaneous Multiclass Classification of tumor types is essential for future clinical implementations of microarray-based cancer diagnosis. In this study, we have combined genetic algorithms (GAs) and all paired support vector machines (SVMs) for Multiclass cancer identification. The predictive features have been selected through iterative SVMs/GAs, and recursive feature elimination post-processing steps, leading to a very compact cancer-related predictive gene set. Leave-one-out cross-validations yielded accuracies of 87.93% for the eight-class and 85.19% for the fourteen-class cancer Classifications, outperforming the results derived from previously published methods.