Similarity-Based Classification in Partially Labeled Networks

We propose a similarity-based method, using the similarity between nodes, to address the problem of classification in partially labeled networks. The basic assumption is that two nodes are more likely to be categorized into the same class if they are more similar. In this paper, we introduce ten similarity indices, including five local ones and five global ones. Empirical results on the co-purchase network of political books show that the similarity-based method can give high accurate classification even when the labeled nodes are sparse which is one of the difficulties in classification. Furthermore, we find that when the target network has many labeled nodes, the local indices can perform as good as those global indices do, while when the data is sparce the global indices perform better. Besides, the similarity-based method can to some extent overcome the unconsistency problem which is another difficulty in classification.Comment: 13 pages,3 figures,1 tabl

Intelligent Fusion of Structural and Citation-Based Evidence for Text Classification

This paper investigates how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity, five derived from the citation structure of the collection, and three measures derived from the structural content, and determine how they can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our empirical experiments using documents from the ACM digital library and the ACM classification scheme show that we can discover similarity functions that work better than any evidence in isolation and whose combined performance through a simple majority voting is comparable to that of Support Vector Machine classifiers

A review of associative classification mining

Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria. Finally, future directions in associative classification, such as incremental learning and mining low-quality data sets, are also highlighted in this paper

Facial Emotional Expressions Of Life-Like Character Based On Text Classifier And Fuzzy Logic

A system consists of a text classifier and Fuzzy Inference System FIS to build a life-like virtual character capable of expressing emotion from a text input is proposed. The system classifies emotional content of sentences from text input and expresses corresponding emotion by a facial expression. Text input is classified using the text classifier while facial expression of the life-like character are controlled by FIS utilizing results from the text classifier. A number of text classifier methods are employed and their performances are evaluated using Leave-One-Out Cross Validation. In real world application such as animation movie the lifelike virtual character of proposed system needs to be animated. As a demonstration examples of facial expressions with corresponding text input as results from the implementation of our system are shown. The system is able to show facial expressions with admixture blending emotions. This paper also describes animation characteristics of the system using neutral expression as center of facial expression transition from one emotion to another. Emotion transition can be viewed as gradual decrease or increase of emotion intensity from one emotion toward other emotion. Experimental results show that animation of lifelike character expressing emotion transition can be generated automatically using proposed system