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7th International Conference on Machine Learning and Cybernetics

作者： Zhang, Zhi-Ying Wang, Jian-Wei Wei, Xiao-Peng Yu, Wen-Jing Dalian Univ Technol Ctr Adv Design Technol Dalian 116622 Peoples R China

ISBN: (纸本)9781424420957

To improve efficiency and quality of case retrieval in case- based reasoning system, a case retrieval model based on the artificial neural network (ANN) and nearest neighbor (NN) algorithm is presented. Firstly, the indexes of cases are created in order to shrink the case-searching range, and the BP neural network is applied to memorize the product cases that are indexed. Secondly, the similar cases, which are retrieved by ANN for the first matching, are computed by NN for the second matching, while the weights of NN are given by customers. Thus, the retrieval efficiency and quality are improved through combining customers' subjective desire with the objective retrieval of ANN. Finally, an example of motorcycle is given to illustrate the working process of the model, which proves the effectiveness and feasibility of case retrieval model.

关键词： case-based reasoning (CBR) BP neural network (BPNN) case retrieval nearest neighbor algorithm

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REGIONAL FOREST ABOVE-GROUND BIOMASS RETRIEVAL BY OPTIMIZED K-NN algorithm IN NORTHEAST CHINA

REGIONAL FOREST ABOVE-GROUND BIOMASS RETRIEVAL BY OPTIMIZED ...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Xin Tian Erxue Chen Zengyuan Li Z. Bob Su Lina Bai Christiaan van der Tol Institute of Forest Resource Information Techniques Chinese Academy of Forestry Faculty of Geo-Information Science and Earth Observation University of Twente

ISBN: (纸本)9781479911127

This study explores retrieval of wall-to-wall forest aboveground biomass (AGB) over Jilin province in Northeast China, using the optimized non-parametric k-NN method, the 7th National Forest Inventory (NFI) data, and the remote sensing data: Landsat-TM/ETM+ images. For pixel-based validation, the estimated result was compared to the NFI data by leave-one-out process and R2 = 0.40 and RMSE = 54.29 tons/hm~2. For county-scale validation, the result was verified by the intensive forest sub-compartment data of eight county and R~2 = 0.80 and RMSE = 34.26 tons/hm~2.

关键词： K-NN method Forest above-ground biomass Optimized configuration retrieval nearest neighbor algorithm Regional Biomass algorithms ROOT-MEAN-SQUARE ERRORS

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Prediction Method of Protein Disulfide Bond Based on Pattern Selection

Prediction Method of Protein Disulfide Bond Based on Pattern...

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The 7th International Conference on Management and Service Science(MASS 2013)

作者： Pengfei Sun Yuanquan Cui Tiankai Chen Ying Zhao College of Computer Science and Technology Harbin Normal University

The effect of the different training samples is different for the classifier when pattern recognition system is *** training samples were selected randomly in the past protein disulfide bond prediction methods,therefore the prediction accuracy of protein contact was *** order to improve the influence of training samples,a prediction method of protein disulfide bond on the basis of pattern selection and Radical Basis Function neural network has been brought forward in this *** attributes related with protein disulfide bond are extracted and coded in the method and pattern selection is used to select training samples from coded samples in order to improve the precision of protein disulfide bond prediction.200 proteins with disulfide bond structure from the PDB database are encoded according to the encoding approach and are taken as models of training *** samples are taken on the pattern selection based on the nearest neighbor algorithm and corresponding prediction models are set by using RBF neural *** simulation experiment result indicates that this method of pattern selection can improve the prediction accuracy of protein disulfide bond.

关键词： Protein Disulfide Bond Neural Network nearest neighbor algorithm Pattern Selection

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Indoor Positioning with Maximum Likelihood Classification of Wi-Fi Signals

Indoor Positioning with Maximum Likelihood Classification of...

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IEEE Sensors Conference

作者： Noah Pritt Frederick Community College Frederick Maryland

ISBN: (纸本)9781467346412

There are many applications for indoor positioning, from navigating hospitals, airports and shopping malls, to mining and disaster response. Despite intensive research, the problem of developing an accurate indoor location system remains largely unsolved. GPS is widely used for outdoor navigation, but its signals are attenuated and useless indoors. Augmented GPS, ultrasound, and inertial navigation systems have been proposed but remain impractical. The solution presented in this paper makes use of commonly available Wi-Fi networks. Implemented as an Android app on an ordinary smart phone, it comprises a calibration stage and a navigation stage. In the calibration stage, the system creates a Wi-Fi fingerprint for each room of a building, where the received signal power of multiple signals are collected over time and space and stored as multivariate Gaussian distributions. During the navigation stage, the system determines its position by matching Wi-Fi signal strengths to the fingerprints with maximum-likelihood classification. Evaluation results in home and commercial environments show that this classification method outperforms the more conventional nearest neighbor algorithm. The resulting system can determine its position in only a few seconds.

关键词： Wi-Fi Classification Methods navigation Inertial navigation systems nearest neighbor algorithm signals shopping mall received signal power Smartphone Positioning systems multiple signal maximum likelihood Gaussian distribution

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MULTI-CRITERIA SEARCH algorithm: AN EFFICIENT APPROXIMATE K-NN algorithm FOR IMAGE RETRIEVAL

MULTI-CRITERIA SEARCH ALGORITHM: AN EFFICIENT APPROXIMATE K-...

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IEEE International Conference on Image Processing

作者： Mehdi Badr Dan Vodislav David Picard Shaoyi Yin Philippe-Henri Gosselin ETIS University of Cergy-Pontoise ENSEA CNRS France TexMex team INRIA Rennes France

ISBN: (纸本)9781479923427

We propose a new method for approximate k-NN search in large scale image databases, based on top-k multi-criteria search techniques. The method defines a simple index structure based on sorted lists, which provides a good compromise between fast retrieval, storage requirements and update cost. The search algorithm delivers approximate results with guarantees about false negatives, with fast emergence of good approximations, monotonically improved and leading if necessary to an exact result. Experiments with the on-disk implementation show that our method produces very good approximate results several times faster than the Baseline method.

关键词： CBIR k-NN search image databases multi-criteria search top-k algorithms visual databases content-based image retrieval SEARCHING algorithm image retrieval retrieval algorithms False Negative nearest neighbor algorithm images Approximate

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Application of the intrinsic time-scale decomposition method to fault diagnosis of wind turbine bearing

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JOURNAL OF VIBRATION AND CONTROL 2012年第2期18卷 240-245页

作者： An, Xueli Jiang, Dongxiang Chen, Jie Liu, Chao Tsinghua Univ Dept Thermal Engn State Key Lab Control & Simulat Power Syst & Gene Beijing 100084 Peoples R China

A fault diagnosis method of wind turbine bearing based on intrinsic time-scale decomposition (ITD) is put forward. In the proposed method, the vibration signal of the main bearing is decomposed into several proper rotation components by the ITD method. The frequency centers of the proper rotation components that contain predominant energy are computed and considered as fault feature vectors. The nearest neighbor algorithm is applied to identify the fault types of the wind turbine bearing. The experimental data of the wind turbine spherical roller bearing in four conditions (normal, outer race fault, inner race fault and roller fault) are applied to evaluate the performance of the proposed method. The results demonstrate the feasibility and accuracy of this approach for the diagnosis of the wind turbine bearing faults under uncertain conditions.

关键词： Fault diagnosis frequency center intrinsic time-scale decomposition nearest neighbor algorithm spherical roller bearing wind turbine

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A MODEL FOR CASE RETRIEVAL BASED ON ANN AND nearest neighbor algorithm

A MODEL FOR CASE RETRIEVAL BASED ON ANN AND NEAREST NEIGHBOR...

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2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

作者： ZHI-YING ZHANG JIAN-WEI WANG XIAO-PENG WEI WEN-JING YU Center for Advanced Design Technology Dalian University Dalian 116622 China

关键词： Case-based reasoning (CBR) BP neural network (BPNN) Case retrieval nearest neighbor algorithm

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Novel anomaly detection technique based on the nearest neighbour and sequential methods

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INSIGHT 2012年第8期54卷 433-435页

作者： Gelman, L. Petrunin, I. Cranfield Univ Cranfield MK43 0AL Beds England

A novel anomaly detection technique based on the nearest neighbour method and the sequential majority method is proposed, developed and validated for damage diagnosis and pattern recognition.

关键词： Damage detection Sequential nearest neighbor anomaly detection inspection technique Pattern recognition nearest neighbor algorithm

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Predict and analyze S-nitrosylation modification sites with the mRMR and IFS approaches

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JOURNAL OF PROTEOMICS 2012年第5期75卷 1654-1665页

作者： Li, Bi-Qing Hu, Le-Le Niu, Shen Cai, Yu-Dong Chou, Kuo-Chen Shanghai Univ Inst Syst Biol Shanghai 200444 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Key Lab Syst Biol Shanghai 200031 Peoples R China Fudan Univ Ctr Computat Syst Biol Shanghai 200433 Peoples R China Gordon Life Sci Inst San Diego CA USA

S-nitrosylation (SNO) is one of the most important and universal post-translational modifications (PTMs) which regulates various cellular functions and signaling events. identification of the exact S-nitrosylation sites in proteins may facilitate the understanding of the molecular mechanisms and biological function of S-nitrosylation. Unfortunately, traditional experimental approaches used for detecting S-nitrosylation sites are often laborious and time-consuming. However, computational methods could overcome this demerit. In this work, we developed a novel predictor based on nearest neighbor algorithm (NNA) with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). The features of physicochemical/biochemical properties, sequence conservation, residual disorder, amino acid occurrence frequency, second structure and the solvent accessibility were utilized to represent the peptides concerned. Feature analysis showed that the features except residual disorder affected identification of the S-nitrosylation sites. It was also shown via the site-specific feature analysis that the features of sites away from the central cysteine might contribute to the S-nitrosylation site determination through a subtle manner. It is anticipated that our prediction method may become a useful tool for identifying the protein S-nitrosylation sites and that the features analysis described in this paper may provide useful insights for in-depth investigation into the mechanism of S-nitrosylation. (C) 2011 Elsevier B.V. All rights reserved.

关键词： S-nitrosylation Maximum relevance minimum redundancy Incremental feature selection nearest neighbor algorithm

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nearest neighbor Distributions for Imbalanced Classification

Nearest Neighbor Distributions for Imbalanced Classification

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International Joint Conference on Neural Networks

作者： Evan Kriminger Jose C. Principe Choudur Lakshminarayan Department of Electrical and Computer Engineering University of Florida HP Labs

ISBN: (纸本)9781467314886

The class imbalance problem is pervasive in machine learning. To accurately classify the minority class, current methods rely on sampling schemes to close the gap between classes, or on the application of error costs to create algorithms which favor the minority class. Since the sampling schemes and costs must be specified, these methods are highly dependent on the class distributions present in the training set. This makes them difficult to apply in settings where the level of imbalance changes, such as in online streaming data. Often they cannot handle multi-class problems. We present a novel single-class algorithm called Class Conditional nearest neighbor Distribution (CCNND), which mitigates the effects of class imbalance through local geometric structure in the data. Our algorithm can be applied seamlessly to problems with any level of imbalance or number of classes, and new examples are simply added to the training set. We show that it performs as well as or better than top sampling and cost-weighting methods on four imbalanced datasets from the UCI Machine Learning Repository, and then apply it to streaming data from the oil and gas industry alongside a modified nearest neighbor algorithm. Our algorithm's competitive performance relative to the state-of-the-art, coupled with its extremely simple implementation and automatic adjustment for minority classes, demonstrates that it is worth further study.

关键词： nearest neighbor nearest neighbor algorithm automatic control minorities geometrical construction Oil and Gas Industry algorithms Streaming multi-class Artificial Intelligence Machine Learning

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