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In this research, we propose a particular version of KNN (K Nearest Neighbor) where the similarity between feature vectors is computed considering the similarity among attributes or features as well as one among values. The task of text summarization is viewed into the binary classification task where each paragraph or sentence is classified into the essence or non-essence, and in previous works,...
In this research, we propose the version of K Nearest Neighbor which considers similarity among attributes for computing the similarity between feature vectors. The text segmentation task is viewed into the binary classification where each pair of sentences or paragraphs is classified into whether we put the boundary or not, and the proposed version resulted in the successful results in previous works...
Text categorization (TC) is an important component in many information organization and information management tasks. Two key issues in TC are feature coding and classifier design. The Euclidean distance is usually chosen as the similarity measure in K-nearest neighbor classification algorithm. All the features of each vector have different functions in describing samples. So we can decide different...
Web page classification poses new research challenges because of the noisy nature of the pages. For the bilingual Chinese-English web pages, it also needs to be considered that how to extract the terms of different languages exactly. A new dictionary-based multilingual text categorization approach is proposed in this paper to try to classify the Chinese-English web pages in specific domain into a...
High-dimensional feature space affects the quality and efficiency of text categorization. This paper investigates an improved genetic algorithm that how to help select relevant features in text classification. We follow the so-called "region growing" method to initialize the population, and uses k-means algorithm to selection operation to control the scope of the search, ensure the validity...
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