Showing papers by "Shirui Pan published in 2010"

Classifier ensemble for uncertain data stream classification

Abstract: Currently available algorithms for data stream classification are all designed to handle precise data, while data with uncertainty or imperfection is quite natural and widely seen in real-life applications. Uncertainty can arise in attribute values as well as in class values. In this paper, we focus on the classification of streaming data that has different degrees of uncertainty within class values. We propose two types of ensemble based algorithms, Static Classifier Ensemble (SCE) and Dynamic Classifier Ensemble (DCE) for mining uncertain data streams. Experiments on both synthetic and real-life data set are made to compare and contrast our proposed algorithms. The experimental results reveal that DCE algorithm outperforms SCE algorithm.

Ensemble of multiple descriptors for automatic image annotation

Abstract: Automatic image annotation (AIA) plays an important role and attracts much research attention in image understanding and retrieval. Annotation can be posed as classification problems where each annotation keyword is defined as a group of database images labeled with a semantic word. It is shown that, by establishing one-to-one corresponding between image region and semantic keyword is a feasible approach for automatic image annotation. In this paper, we proposed a novel algorithm, EMDAIA for automatic image annotation based on ensemble of descriptors. EMDAIA regards the annotation process as a multi-class image classification. The producers of EMDAIA are presented as follows. First, each image is segmented into a collection of image regions. For each region, a variety of low-level visual descriptors are extracted. All regions are then clustered into k categories with each cluster associated with an annotation keyword. Moreover, for an unlabeled instance, distance between this instance and each cluster center is measured and the nearest category's keyword is chosen to annotate it. Experiment results on LabelMe, a benchmark dataset, shows EMDAIA outperforms some recent state-of-the-art automatic image annotation algorithms.