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Bin-Bin Yang

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3 papers

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AIJ Journal 2021 Journal Article

On the noise estimation statistics

Wei Gao
Teng Zhang
Bin-Bin Yang
Zhi-Hua Zhou

Learning with noisy labels has attracted much attention during the past few decades. A fundamental problem is how to estimate noise proportions from corrupted data. Previous studies on this issue resort to the estimations of class distributions, conditional distributions, or the kernel embedding of distributions. In this paper, we present another simple and effective approach for noise estimation. The basic idea is to utilize the first- and second-order statistics of observed data, and the positive semi-definiteness of covariance matrices. Then, an upper bound on noise estimation is provided without additional assumptions over data distribution. Based on this idea and using the locality property of random noise, we develop the Noise Estimation Statistics with Clusters (NESC) method, which firstly clusters the corrupted data by k-means algorithm, and then makes noise estimation from clusters based on the first- and second-order statistics. We present the existence, uniqueness and convergence analysis of our noise estimation, and empirical studies verify the effectiveness of the NESC method.

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AAAI Conference 2020 Conference Paper

AUC Optimization with a Reject Option

Song-Qing Shen
Bin-Bin Yang
Wei Gao

Making an erroneous decision may cause serious results in diverse mission-critical tasks such as medical diagnosis and bioinformatics. Previous work focuses on classiﬁcation with a reject option, i. e. , abstain rather than classify an instance of low conﬁdence. Most mission-critical tasks are always accompanied with class imbalance and cost sensitivity, where AUC has been shown a preferable measure than accuracy in classiﬁcation. In this work, we propose the framework of AUC optimization with a reject option, and the basic idea is to withhold the decision of ranking a pair of positive and negative instances with a lower cost, rather than mis-ranking. We obtain the Bayes optimal solution for ranking, and learn the reject function and score function for ranking, simultaneously. An online algorithm has been developed for AUC optimization with a reject option, by considering the convex relaxation and plug-in rule. We verify, both theoretically and empirically, the effectiveness of the proposed algorithm.

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AAAI Conference 2019 Conference Paper

Weighted Oblique Decision Trees

Bin-Bin Yang
Song-Qing Shen
Wei Gao

Decision trees have attracted much attention during the past decades. Previous decision trees include axis-parallel and oblique decision trees; both of them try to find the best splits via exhaustive search or heuristic algorithms in each iteration. Oblique decision trees generally simplify tree structure and take better performance, but are always accompanied with higher computation, as well as the initialization with the best axis-parallel splits. This work presents the Weighted Oblique Decision Tree (WODT) based on continuous optimization with random initialization. We consider different weights of each instance for child nodes at all internal nodes, and then obtain a split by optimizing the continuous and differentiable objective function of weighted information entropy. Extensive experiments show the effectiveness of the proposed algorithm.

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