Hongliang Fei

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Boosting is a very successful classification algorithm that produces a linear combination of "weak" classifiers (a.k.a. base learners) to obtain high quality classification models. In this paper we propose a new boosting algorithm where base learners have structure relationships in the functional space. Though such relationships are generic, our work is(More)
With the development of highly efficient graph data collection technology in many application fields, classification of graph data emerges as an important topic in the data mining and machine learning community. Towards building highly accurate classification models for graph data, here we present an efficient graph feature selection method. In our method,(More)
Determining anomalies in data streams that are collected and transformed from various types of networks has recently attracted significant research interest. Principal Component Analysis (PCA) has been extensively applied to detecting anomalies in network data streams. However, none of existing PCA based approaches addresses the problem of identifying the(More)
Determining anomalies in data streams that are collected and transformed from various types of networks has recently attracted significant research interest. Principal component analysis (PCA) has been extensively applied to detecting anomalies in network data streams. However, none of existing PCA-based approaches addresses the problem of identifying the(More)
Multi-task learning (MTL) aims to enhance the generalization performance of supervised regression or classification by learning multiple related tasks simultaneously. In this paper, we aim to extend the current MTL techniques to high dimensional data sets with structured input and structured output (SISO), where the SI means the input features are(More)
We consider the problem of active learning when the categories are represented as a tree with leaf nodes as outputs and internal nodes as clusters of the outputs at multiple granularity. Recent work has improved the traditional techniques by moving beyond ”flat” structure through incorporation of the label hierarchy into the uncertainty measure. However,(More)
With increasing number of chemicals produced each year, it still remains a daunting task to keep up with the toxicity profile of each chemical. In this paper, we attempt to predict toxicity of compounds using computational techniques, where results from certain in vitro assays applied on 309 chemicals, along with computed properties of chemicals are used to(More)
Information Flow Studies analyze the principles and mechanisms of social information distribution and is an essential research topic in social networks. Traditional approaches are primarily based on the social network graph topology. However, topology itself can not accurately reflect the user interests or activities. In this paper, we adopt a(More)