Logitboost is an influential boosting algorithm for classification. In this paper, we develop robust logitboost to provide an explicit formulation of tree-split criterion for building weak learners (regression trees) for logitboost. This formulation leads to a numerically stable implementation of logitboost. We then pr…
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11 results for “LogitBoost”
This paper presents an improvement to model learning when using multi-class LogitBoost for classification. Motivated by the statistical view, LogitBoost can be seen as additive tree regression. Two important factors in this setting are: 1) coupled classifier output due to a sum-to-zero constraint, and 2) the dense Hess…
LogitBoost improves autoregressive models for binary data.
problem Efficiently modeling multivariate binary distributions.
method Training separate probability estimators for each dimension using LogitBoost.
result Separate probability estimators can achieve state-of-the-art performance.
We analyze bias-variance of margin losses.
problem Understanding model overfitting/underfitting.
method Bias-variance decomposition for strictly convex margin losses.
result Expected risk decomposes into central model risk and data variation.
SPLBoost improves robustness of AdaBoost by incorporating self-paced learning.
problem AdaBoost's sensitivity to random noise/outliers.
method Integrating self-paced learning into Boosting framework.
result SPLBoost achieves improved robustness compared to traditional Boosting algorithms.
Proposes tunable GMM kernels for classification tasks.
problem Lack of competitive performance of GMM kernels compared to tree methods on deep learning datasets.
method Introduces tunable GMM kernels with added parameters and combines basic kernels for improved performance.
result Tunable GMM kernels can produce good results for various classification tasks.
Tunable GMM Kernelsstat.ML
Tunable GMM kernels improve on original GMM in various classification tasks.
problem Improving the efficiency and performance of GMM kernels.
method Developed three tunable GMM kernels: eGMM, pGMM, and epGMM.
result Tunable GMM kernels typically improve over the original GMM kernel on 60 datasets.
A new gradient tree boosting framework reduces variance and accelerates performance.
problem High variance in stochastic gradient boosting.
method Combining gradient tree boosting with importance sampling and a regularizer.
result Achieves a linear convergence rate on logistic loss and 2.5x--18x acceleration on LogitBoost and LambdaMART.
Early stopping for kernel boosting algorithms: A general analysis with localized complexitiesstat.ML
The paper analyzes early stopping for boosting algorithms using localized Gaussian complexity.
problem Understanding the performance of early stopping in kernel boosting algorithms.
method Direct connection between stopped iterate performance and localized Gaussian complexity of function classes.
result Optimal stopping rules derived for various kernel classes, showing correspondence with practice.
Abc-boost is a new line of boosting algorithms for multi-class classification, by utilizing the commonly used sum-to-zero constraint. To implement abc-boost, a base class must be identified at each boosting step. Prior studies used a very expensive procedure based on exhaustive search for determining the base class at …
Study uses machine learning to identify IBD biomarkers from gut microbiota.
problem Identifying biomarkers for Inflammatory Bowel Disease (IBD) from gut microbiota.
method Ensemble feature selection methods (CMIM, FCBF, mRMR, XGBoost) applied to IBD-associated metagenomics dataset.
result XGBoost minimizes microbiota used for IBD diagnosis, improving classification accuracy.