Zhi-Zhuo Zhang

MOTTO:

“What is the meaning of life is to find out what it ought to be !”

Research Interest:

I have wide research interests, mainly including artificial intelligence, statistical machine learning, data mining, bioinformatics, information retrieval, nonlinear Embedding, Mathematics Modeling. And Recently, I may focus following problems: “Non-Convex Optimization and Imbalance Learning”, “Efficient Semi-Supervised Learning with Fenchel Duality ”, “Nowhere Differentiable Functions in Learning”. I will be so glad if someone has any idea of these topics and shares with me.

My Thesis is available now (New!)

《基于损失函数的不平衡分类问题的研究》(A Study of Imbalance Classification Problem based on Loss Functions) (PDF 2.2M)

Data imbalance is considered as an important factor affecting the performance of classifiers. Many Meta methods like Re-sampling, classifiers ensemble, various evaluation, have been tried to handle the imbalance problem. This paper takes the machine learning problem as an optimization problem based on Tikhonov Regularization framework, and discusses the effect of different loss function on the optimized solutions in the imbalance situation. In this paper, Classified Discussion of three type convex loss function further points out that the essential cause is the convex optimization, which leads to the performance degradation of the classifiers in the imbalance situation. Moreover, the “imbalance insensitive” and “imbalance insensitive” loss function are defined in this paper with their sufficient condition. However, the “imbalance insensitive” loss function is non-convex function, which turns the machine learning problem to a non-convex optimizing problem. Hence, the further analysis on the non-convex problem solving methods like random gradient decreasing and semi-define programming approximating are given in this paper too. Finally, the paper generalizes the “imbalance insensitive” theory in the multi-class case and cost-sensitive case and makes some discussion on the issue “sampling imbalance”.

Keywords: data imbalance, Tikhonov Regularization, non-convex optimizing, imbalance insensitive, loss function, semi-define programming