Automatically identifying the research areas of academic/industry researchers is an important task for building expertise organizations or search systems. In general, this task can be viewed as text classification that generates a set of research areas given the expertise of a researcher like documents of publications. However, this task is challenging because the evidence of a research area may only exist in a few documents instead of all documents. Moreover, the research areas are often organized in a hierarchy, which limits the effectiveness of existing text categorization methods. This paper proposes a novel approach, Multi-instance Learning of Hierarchical Multi-label Classification Model (MIHML) for the task, which effectively identifies multiple research areas in a hierarchy from individual documents within the profile of a researcher. An Expectation-Maximization (EM) optimization algorithm is designed to learn the model parameters. Extensive experiments have been conducted to demonstrate the superior performance of proposed research with a real world application.

The Berkeley Drosophila Genome Project (BDGP) has produced a large number of gene expression patterns, many of which have been annotated textually with anatomical and developmental terms. These terms spatially correspond to local regions of the images; however, they are attached collectively to groups of images, such that it is unknown which term is assigned to which region of which image in the group. This poses a challenge to the development of the computational method to automate the textual description of expression patterns contained in each image. In this paper, we show that the underlying nature of this task matches well with Figure 1: Samples of images and associated annotation terms a new machine learning framework, Multi-Instance of the gene Actn in the stage ranges 11-12 and 13-16 in the Multi-Label learning (MIML). We propose a new BDGP database. The darkly stained region highlights the MIML support vector machine to solve the problems place where the gene is expressed. The darker the region, that beset the annotation task.

Such a problem can occur in many real-world tasks, e.g. an image usually