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ARS k NN-A k -NN Classifier Using Mass Based Similarity Measure

Procedia Computer Science 2015年 46卷 457-462页

作者： Ashish Kumar Roheet Bhatnagar Sumit Srivastava Manipal University Jaipur Dehmi Kalan Jaipur 303007 India

k -Nearest Neighbor ( k -NN) classification technique is one of the most elementary and straightforward classification methods. Although distance learning is in the core of k -NN classification, similarity can be preferred upon distance in several practical applications. This paper proposes a novel algorithm for learning a class of an instance based on a similarity measure which does not calculate distance, for k -Nearest Neighbor ( k -NN) classification.

关键词： classification algorithm Data Mining Nearest Neighbor Similarity Measures

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AUTOMATIC VEHICLE classification IN SYSTEMS WITH SINGLE INDUCTIVE LOOP DETECTOR

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METROLOGY AND MEASUREMENT SYSTEMS 2014年第4期21卷 619-630页

作者： Gajda, J. Mielczarek, M. AGH Univ Sci & Technol Dept Measurement & Elect PL-30059 Krakow Poland

The work proposes a new method for vehicle classification, which allows treating vehicles uniformly at the stage of defining the vehicle classes, as well as during the classification itself and the assessment of its correctness. The sole source of information about a vehicle is its magnetic signature normalised with respect to the amplitude and duration. The proposed method allows defining a large number (even several thousand) of classes comprising vehicles whose magnetic signatures are similar according to the assumed criterion with precisely determined degree of similarity. The decision about the degree of similarity and, consequently, about the number of classes, is taken by a user depending on the classification purpose. An additional advantage of the proposed solution is the automated defining of vehicle classes for the given degree of similarity between signatures determined by a user. Thus the human factor, which plays a significant role in currently used methods, has been removed from the classification process at the stage of defining vehicle classes. The efficiency of the proposed approach to the vehicle classification problem was demonstrated on the basis of a large set of experimental data.

关键词： inductive loop detector magnetic signature vehicle classification classification algorithm

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Patterns of brain structural connectivity differentiate normal weight from overweight subjects

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NEUROIMAGE-CLINICAL 2015年 7卷 506-517页

作者： Gupta, Arpana Mayer, Emeran A. Sanmiguel, Claudia P. Van Horn, John D. Woodworth, Davis Ellingson, Benjamin M. Fling, Connor Love, Aubrey Tillisch, Kirsten Labus, Jennifer S. Univ Calif Los Angeles Gail & Gerald Oppenheimer Family Ctr Neurobiol St IBOP Los Angeles CA USA Univ Calif Los Angeles David Geffen Sch Med Los Angeles CA 90095 USA Univ Calif Los Angeles Div Digest Dis Los Angeles CA USA Univ Calif Los Angeles Ahmanson Lovelace Brain Mapping Ctr Los Angeles CA USA Univ Calif Los Angeles GLA VHA Integrat Med Los Angeles CA USA Univ So Calif Keck Sch Med Inst Neuroimaging & Informat Los Angeles CA 90033 USA Univ Calif Los Angeles David Geffen Sch Med Radiol Los Angeles CA 90095 USA

Background: Alterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks. Aim: To apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements. Methods: Structural images (N = 120, overweight N = 63) and diffusion tensor images (DTI) (N = 60, overweight N = 30) were obtained from healthy control subjects. For the total sample the mean age for the overweight group (females = 32, males = 31) was 28.77 years (SD = 9.76) and for the normal weight group (females = 32, males = 25) was 27.13 years (SD = 9.62). Regional segmentation and parcellation of the brain images was performed using Freesurfer. Deterministic tractography was performed to measure the normalized fiber density between regions. A multivariate pattern analysis approach was used to examine whether brain measures can distinguish overweight from normal weight individuals. Results: 1. White-matter classification: The classification algorithm, based on 2 signatures with 17 regional connections, achieved 97% accuracy in discriminating overweight individuals from normal weight individuals. For both brain signatures, greater connectivity as indexed by increased fiber density was observed in overweight compared to normal weight between the reward network regions and regions of the executive control, emotional arousal, and somatosensory networks. In contrast, the opposite pattern (decreased fiber density) was found between ventromedial prefrontal cortex and the anterior insula, and between thalamus and executive control network regions. 2. Gray-matter classification: The classification algorithm, based on 2 signatures with 42 morphological features, achi

关键词： Obesity Overweight Morphological gray-matter Anatomical white-matter connectivity Reward network Multivariate analysis classification algorithm

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classification algorithm sensitivity to training data with non representative attribute noise

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DECISION SUPPORT SYSTEMS 2009年第3期46卷 743-751页

作者： Mannino, Michael Yang, Yanjuan Ryu, Young Univ Colorado Sch Business Denver CO 80217 USA Univ Texas Dallas Sch Management Richardson TX 75083 USA

We present an empirical comparison of classification algorithms when training data contains attribute noise levels not representative of field data. To study algorithm sensitivity, we develop an innovative experimental design using noise situation, algorithm, noise level, and training set size as factors. Our results contradict conventional wisdom indicating that investments to achieve representative noise levels may not be worthwhile. ill general, over representative training noise Should be avoided while under representative training noise is less of a concern. However, interactions among algorithm, noise level, and training set size indicate that these general results may not apply to particular practice situations. (c) 2008 Elsevier B.V. All rights reserved.

关键词： Attribute noise Area under the Receiver Operating Curve classification algorithm

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Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2013年 96卷 173-179页

作者： Romero, Jose R. Roncallo, Pablo F. Akkiraju, Pavan C. Ponzoni, Ignacio Echenique, Viviana C. Carballido, Jessica A. CCT Bahia Blanca CERZOS CONICET Buenos Aires DF Argentina UNS Dept Agron Buenos Aires DF Argentina UNS DCIC Lab Invest & Desarrollo Comp Cient Buenos Aires DF Argentina CCT Bahia Blanca Planta Piloto Ingn Quim PLAPIQUI CONICET Bahia Blanca Buenos Aires Argentina

Wheat is one of the most important cereals worldwide for human nutrition. Tetraploid wheat (Triticum turgidum L. asp. durum, 2n = 28, genomes AABB) is mainly used to produce pasta. The main objective of durum wheat breeding programs is to develop varieties with good quality and high yields. Yield is a very complex trait, and depends on different yield components that are genetically controlled and affected by environmental constraints. In this context, machine learning constitutes an excellent alternative for the analysis of a high number of traits in order to extract the most relevant ones as confident predictors of the performance of this crop, allowing a better agricultural planning. Thus, we propose the use of machine learning algorithms for the classification of yield components and for the search of new rules to infer high yields at harvest of durum wheat. The main objective of this work was to obtain rules for predicting durum wheat yield through different machine learning algorithms, and compare them to detect the one that best fits the model. In order to achieve this goal, One-R, J48, Ibk and A priori algorithms were run with data collected by our research group of a RIL (recombinant inbreed lines) population growing in six different environments from the Province of Buenos Aires in Argentina. The results indicate that the A priori method obtains the best performance for all locations, and the classificators generated using the different algorithms share a common set of selected traits. Moreover, comparing these results with the previous ones obtained using different techniques, mainly QTL mapping, the traits indicated to be the most significant ones were the same. The analysis of the resulting rules shows the soundness in the agronomic relevance of the extracted knowledge. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Machine learning Expert system classification algorithm Yield Triticum turgidum Pasta wheat

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The Application of Negative Selection algorithm in Multi-angle Infrared Vehicle Images Recognition

The Application of Negative Selection Algorithm in Multi-ang...

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International Conference on Telecommunications and Signal Processing

作者： Xi Yu Dongmei Fu Tao Yang Kamil Riha School of Information and Engeering University of Science and Technology Beijing Department of Telecommunications Brno University of Technology

ISBN: (纸本)9781479984992

At present, the objects of the vehicle recognition based on image processing are mostly visible images with rich details and limited camera angles. Few studies have been done on the recognition of multi-angle infrared vehicle images with less detail. However, it has important implications for transportation and especially public security. This paper extracts object contour of the vehicle from infrared vehicle images. After image correction and normalization, the geometric features and moment invariants are extracted. Based on the tolerance process of biological immune cells, this paper proposes a new Multi-classification Artificial Immunity Negative Selection (MAINS) algorithm. This algorithm can recognize vehicle types by the objects' geometric features and moment invariants. It has a stable performance and global search ability, also a high correct recognition rate in infrared vehicle image recognition.

关键词： classification algorithm Infrared Images Negative Selection Vehicle Recognition

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The Study of Automatic Machine Image Monitoring of Floating Waste Bottles

The Study of Automatic Machine Image Monitoring of Floating ...

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2nd International Conference on Simulation and Modeling Methodologies,Technologies and Applications(SMTA 2015)

作者： Yong-xin Qin Rong Hu Zhao-min Ma Bo Hu Yu-qiu Shi College of Electronic & Information Engineering Guangxi University of Science and Technology

Floating bottles is one of the most common pollutant of our water. As automatic monitoring is the basis of prompt and effective waste management, a new method of automatic machine image monitoring is proposed. The original image on the surface of the water is firstly segmented, then the segmented image is processed with 5 morphological operations including dilation, closing, skeleton, skeleton after dilation and skeleton after closing. Then 6 areas characteristics are extracted from the segmented images and the 5 morphological images. Finally, the need for the salvage of the floating bottles is identified with the 6 areas characteristics based on the KNN algorithm. There are 80 images used as training samples and 50 images used as test samples in the experiments. The results of the experiments show that compared with manual identification, the accuracy rates of the training samples and test samples are always 90% higher with different KNN algorithm parameters. This means that the method is effective for the automatic monitoring of floating bottles on the water surfaces and should be helpful for the automatic salvage of waste floating bottles.

关键词： Floating bottle machine vision K Nearest Neighbour(KNN) classification algorithm

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A Hybrid Monitoring Method for Robust Multivariate Process Control

A Hybrid Monitoring Method for Robust Multivariate Process C...

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SCIEI 2015 Singapore Conference

作者： Sung Ho Park Seoung Bum Kim Korea University/ Department of Industrial Management Engineering

Control chart,the most representative statistical process control technique,can be considered to share a common goal with classification algorithms in that both of them aim at estimating the decision boundaries that classify the observations into either in-control or the out-of-control *** main limitation that conventional control chart cannot take advantage of using out-of-control information can be eased by integrating classification algorithms with existing monitoring *** the present study,we proposed a hybrid scheme that combines T2 control chart and binary class classification algorithms by eliciting the nature of class *** and real case studies demonstrated that the proposed method can be effectively used for monitoring multivariate processes when their distributions are nonnormally distributed or unknown.

关键词： Multivariate control chart classification algorithm Hybrid monitoring statistic

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Reverse phase protein array based tumor profiling identifies a biomarker signature for risk classification of hormone receptor-positive breast cancer

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Translational Proteomics 2014年第1期2卷 52-59页

作者： Sonntag, Johanna Bender, Christian Soons, Zita der Heyde, Silvia von König, Rainer Wiemann, Stefan Sinn, Hans-Peter Schneeweiss, Andreas Beißbarth, Tim Korf, Ulrike Division of Molecular Genome Analysis German Cancer Research Center (DKFZ) Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Network Modeling Leibniz Institute for Natural Product Research and Infection Biology Hans Knöll Institute Jena Jena Germany Department of Medical Statistics University Medical Center Göttingen Göttingen Germany Integrated Research and Treatment Center Center for Sepsis Control and Care (CSCC) Jena University Hospital Jena Germany Department of Pathology University of Heidelberg Heidelberg Germany National Center for Tumor Diseases (NCT) University of Heidelberg Heidelberg Germany TRON - Translational Oncology Johannes Gutenberg University of Mainz Medicine Mainz Germany Department of Knowledge Engineering Maastricht University Maastricht Netherlands

A robust subclassification of luminal breast cancer, the most common molecular subtype of human breast cancer, is crucial for therapy decisions. While a part of patients is at higher risk of recurrence and requires chemo-endocrine treatment, the other part is at lower risk and also poorly responds to chemotherapeutic regimens. To approximate the risk of cancer recurrence, clinical guidelines recommend determining histologic grading and abundance of a cell proliferation marker in tumor specimens. However, this approach assigns an intermediate risk to a substantial number of patients and in addition suffers from a high interobserver variability. Therefore, the aim of our study was to identify a quantitative protein biomarker signature to facilitate risk classification. Reverse phase protein arrays (RPPA) were used to obtain quantitative expression data for 128 breast cancer relevant proteins in a set of hormone receptor-positive tumors (n = 109). Proteomic data for the subset of histologic G1 (n = 14) and G3 (n = 22) samples were used for biomarker discovery serving as surrogates of low and high recurrence risk, respectively. A novel biomarker selection workflow based on combining three different classification methods identified caveolin-1, NDKA, RPS6, and Ki-67 as top candidates. NDKA, RPS6, and Ki-67 were expressed at elevated levels in high risk tumors whereas caveolin-1 was observed as downregulated. The identified biomarker signature was subsequently analyzed using an independent test set (AUC = 0.78). Further evaluation of the identified biomarker panel by Western blot and mRNA profiling confirmed the proteomic signature obtained by RPPA. In conclusion, the biomarker signature introduced supports RPPA as a tool for cancer biomarker discovery. © 2014 The Authors.

关键词： Biomarker classification algorithm Luminal breast cancer Reverse phase protein array Risk classification

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AN APPLICATION OF MULTIPLE SPACE NEAREST NEIGHBOR CLASSIFIER IN LAND COVER classification

AN APPLICATION OF MULTIPLE SPACE NEAREST NEIGHBOR CLASSIFIER...

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IEEE Joint International Geoscience and Remote Sensing Symposium (IGARSS) / 35th Canadian Symposium on Remote Sensing

作者： Martins-Bede, Flavia de Toledo Reis, Mariane Souza Pantaleao, Eliana Dutra, Luciano Sandri, Sandra Brazilian Natl Inst Space Res INPE Sao Jose Dos Campos Brazil

ISBN: (纸本)9781479957750

This work presents a case study in land cover classification using ms-NN, an extension of k-NN classification algorithm. The case study focuses on an area in the Brazilian Amazon region, with data obtained from LANDSAT5 satellite Thematic Mapper (TM) sensor and Advanced Land Observing System satellite (ALOS) Phase Array L-Band Synthetic Aperture Radar (PALSAR), using Fine Beam Dual. The results obtained with ms-NN are compared with k-NN and Support Vector Machine algorithms, considering the use of a single training set, a Monte Carlo procedure for testing and an extensive number of parameterizations for the classification methods. Considering only the best results for each classifier, ms-NN obtained better results than the other methods.

关键词： land cover classification multiple space nearest neighbor classification algorithm SAR and optical data classification

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