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Intelligent Analysis and Implement of Compliant Information Based on Filter Technology

Author: YuanChunYan
Tutor: LiAiHua
School: Shenyang University of Technology
Course: Computer Software and Theory
Keywords: Spam messages Intelligence analysis Chinese word segmentation Feature Extraction Minimum risk
CLC: TP391.1
Type: Master's thesis
Year: 2011
Downloads: 19
Quote: 0
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Abstract


In this paper, SMS filtering application strategy built on the basis of the statistical theory the Bayse classification algorithm based on the complaint platform for complaints of adverse SMS as a research object, intelligent analysis and research on them, explicitly use category complaints information to guide data analysis carried out, to extract information from these complaints, the characteristics of the spam messages, and analysis for category unknown complaint information, the overall analysis results in tabular form, submitted to the relevant processing sector as a treatment basis, to address the human The reported analysis difficulty sudden. The existing SMS filtering system, the actual expansion of the keyword-based text classification, text classification has shortcomings, there are also: fixed dictionary extracted keywords, can not adapt to the word flexible change; one by one sampling analysis to generate a classifier, the classifier's accuracy can not be guaranteed when the test data and sample differences; ensure the reliability of the system in the overall point of view, does not take into account the risk of the keyword extraction process, therefore, from the system flexibility, versatility and accuracy of three existing filtering policies to perfect a viable and effective solution, the main contents are as follows: (1) the flexibility of the system: (1) extraction of fundamental Keywords: Basic longest matching word string fuzzy matching combination, look for times long term only if the basic matching and fuzzy matching fails. (2) extract feature words: concentration within the class text classification, class dispersion and the class average degree and weighted sum thinking combine in order to raise the word the classification contribution to the existing SMS filtering down Invensys has the accuracy and comprehensiveness of the classification rules. (2) classification of versatility: the application of probability theory in the theory of random acquisition sample set, avoid classifier over-fit the sample data, it has versatility. (3) the accuracy of the classifier: binary classification problem using the minimum risk thinking follows expansion, so that the system has a risk minimization: ① It is used for multi-class classification problem; the ② keyword extraction. Based on the complaint information intelligent analysis strategy, to achieve a flexible, efficient, and high accuracy the complaint reporting platform system, the test of the experimental data to prove the three really improved compared to the previous strategy.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Text Processing
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