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KNNModel algorithm and its application

Author: HuangJie
Tutor: GuoGongDe
School: Fujian Normal University
Course: Applied Computer Technology
Keywords: k-nearest neighbor KNN model Classification Incremental Algorithm Intrusion Detection Signal peptide prediction
CLC: TP311.13
Type: Master's thesis
Year: 2011
Downloads: 26
Quote: 0
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k-nearest neighbor (KNN) algorithm is a simple and effective classification algorithm . Traditional KNN classification algorithm parameter k is difficult to determine the existence of new data and classification time consuming large two defects. kNN model algorithm ( abbreviated KNNModel) is based on the principle of KNN classification algorithm , which overcomes the traditional KNN classification algorithm of these two defects. KNNModel supervision to build data through multiple KNN model clusters , which replaces the original dataset as a basis for classification . Not only reduces the dependence on the parameter k , but also improve the speed and accuracy of classification . This paper discusses the characteristics and problems of KNNModel , its improvements and expansion , and these improved algorithms are applied to intrusion detection, signal peptide prediction and other fields. Its main tasks are : ( 1 ) Based on KNN model incremental learning algorithm (IKNNModel), through the new training samples are generated on different \( 2 ) based on incremental KNN model for distributed intrusion detection architecture, KNNModel algorithm is applied to the field of intrusion detection , and through distributed parallel technology to improve the efficiency and accuracy of the algorithm . ( 3 ) subspace based classification algorithm (FSub), for each class in a different feature subspaces each cluster to generate a model , and for classification . ( 4 ) a multi- classifier integrated signal peptide prediction method will FSub signal peptide prediction algorithm is applied to the field , and through a multi- classifier fusion methods to improve prediction accuracy . In some public data sets experimental results show that the algorithm of this paper, on the validity of KNNModel .

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