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Dissertations / Theses on the topic 'Data Classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Morshedzadeh, Iman. "Data Classification in Product Data Management." Thesis, Högskolan i Skövde, Institutionen för teknik och samhälle, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-14651.

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This report is about the product data classification methodology that is useable for the Volvo Cars Engine (VCE) factory's production data, and can be implemented in the Teamcenter software. There are many data generated during the life cycle of each product, and companies try to manage these data with some product data management software. Data classification is a part of data management for most effective and efficient use of data. With surveys that were done in this project, items affecting the data classification have been found. Data, attributes, classification method, Volvo Cars Engine f

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2

Currie, Sheila. "Data classification for choropleth mapping." Thesis, University of Ottawa (Canada), 1989. http://hdl.handle.net/10393/5725.

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Gómez, Juan Martínez. "Automatic classification of neural data." Thesis, University of Leicester, 2011. http://hdl.handle.net/2381/9696.

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In this thesis we present a new solution for an automatic classification of the single-neuron activity. The study of the computational role of individual neurons underlying different cognitive process is a gold standard in Neuroscience. This type of analysis is done first, by recording the extracellular spikes of the neurons near the tip of a microelectrode and second, by isolating the spikes of the recorded cells based on the similarity of their shapes using a method called spike sorting. In recent years, important advances in microelectrode technology allow us now to perform massive parallel

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4

Pötzelberger, Klaus, and Helmut Strasser. "Data Compression by Unsupervised Classification." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1997. http://epub.wu.ac.at/974/1/document.pdf.

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This paper deals with a general class of classification methods which are related both to vector quantization in the sense of Pollard, [12], as well as to competitive learning in the sense of Kohonen, [10]. The basic duality of minimum variance partitioning and vector quantization known from statistical cluster analysis is shown to be true for this whole class of classification problems. The paper contains theoretical results like existence of optima, consistency of approximate optima and characterization of local optima as fixpoints of a fix point algorithm. A fix point algorithm is proposed

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Soukhoroukova, Nadejda. "Data classification through nonsmooth optimization." Thesis, University of Ballarat [Mt. Helen, Vic.] :, 2003. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/42220.

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Kröger, Viktor. "Classification in Functional Data Analysis : Applications on Motion Data." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184963.

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Anterior cruciate knee ligament injuries are common and well known, especially amongst athletes.These injuries often require surgeries and long rehabilitation programs, and can lead to functionloss and re-injuries (Marshall et al., 1977). This work aims to explore the possibility of applyingsupervised classification on knee functionality, using different types of models, and testing differentdivisions of classes. The data used is gathered through a performance test, where individualsperform one-leg hops with motion sensors attached to their bodies. The obtained data representsthe position over

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7

Lan, Liang. "Data Mining Algorithms for Classification of Complex Biomedical Data." Diss., Temple University Libraries, 2012. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/214773.

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Computer and Information Science<br>Ph.D.<br>In my dissertation, I will present my research which contributes to solve the following three open problems from biomedical informatics: (1) Multi-task approaches for microarray classification; (2) Multi-label classification of gene and protein prediction from multi-source biological data; (3) Spatial scan for movement data. In microarray classification, samples belong to several predefined categories (e.g., cancer vs. control tissues) and the goal is to build a predictor that classifies a new tissue sample based on its microarray measurements. When

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Lozano, Albalate Maria Teresa. "Data Reduction Techniques in Classification Processes." Doctoral thesis, Universitat Jaume I, 2007. http://hdl.handle.net/10803/10479.

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The learning process consists of different steps: building a Training Set (TS), training the system, testing its behaviour and finally classifying unknown objects. When using a distance based rule as a classifier, i.e. 1-Nearest Neighbour (1-NN), the first step (building a training set) includes editing and condensing data. The main reason for that is that the rules based on distance need many time to classify each unlabelled sample, x, as each distance from x to each point in the training set should be calculated. So, the more reduced the training set, the shorter the time needed for each new

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Aygar, Alper. "Doppler Radar Data Processing And Classification." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609890/index.pdf.

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In this thesis, improving the performance of the automatic recognition of the Doppler radar targets is studied. The radar used in this study is a ground-surveillance doppler radar. Target types are car, truck, bus, tank, helicopter, moving man and running man. The input of this thesis is the output of the real doppler radar signals which are normalized and preprocessed (TRP vectors: Target Recognition Pattern vectors) in the doctorate thesis by Erdogan (2002). TRP vectors are normalized and homogenized doppler radar target signals with respect to target speed, target aspect angle and target ra

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10

Lee, Ho-Jin. "Functional data analysis: classification and regression." Texas A&M University, 2004. http://hdl.handle.net/1969.1/2805.

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Functional data refer to data which consist of observed functions or curves evaluated at a finite subset of some interval. In this dissertation, we discuss statistical analysis, especially classification and regression when data are available in function forms. Due to the nature of functional data, one considers function spaces in presenting such type of data, and each functional observation is viewed as a realization generated by a random mechanism in the spaces. The classification procedure in this dissertation is based on dimension reduction techniques of the spaces. One commonly used metho

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Fernandez, Noemi. "Statistical information processing for data classification." FIU Digital Commons, 1996. http://digitalcommons.fiu.edu/etd/3297.

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This thesis introduces new algorithms for analysis and classification of multivariate data. Statistical approaches are devised for the objectives of data clustering, data classification and object recognition. An initial investigation begins with the application of fundamental pattern recognition principles. Where such fundamental principles meet their limitations, statistical and neural algorithms are integrated to augment the overall approach for an enhanced solution. This thesis provides a new dimension to the problem of classification of data as a result of the following developments: (1

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Bocancea, Andreea. "Supervised Classification Leveraging Refined Unlabeled Data." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119320.

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This thesis focuses on how unlabeled data can improve supervised learning classi-fiers in all contexts, for both scarce to abundant label situations. This is meant toaddress the limitations within supervised learning with regards to label availability.Extending the training set with unlabeled data can overcome issues such as selec-tion bias, noise and insufficient data. Based on the overall data distribution andthe initial set of labels, semi-supervised methods provide labels for additional datapoints. The semi-supervised approaches considered in this thesis belong to one ofthe following categ

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Langdon, Matthew James. "Classification of images and censored data." Thesis, University of Leeds, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434618.

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NUNES, BERNARDO PEREIRA. "AUTOMATIC CLASSIFICATION OF SEMI-STRUCTURED DATA." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=14382@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>O problema da classificação de dados remonta à criação de taxonomias visando cobrir áreas do conhecimento. Com o surgimento da Web, o volume de dados disponíveis aumentou várias ordens de magnitude, tornando praticamente impossível a organização de dados manualmente. Esta dissertação tem por objetivo organizar dados semi-estruturados, representados por frames, sem uma estrutura de classes prévia. A dissertação apresenta um algoritmo, baseado no K-Medóide, capaz de organizar um conjunto de frames em classes, estruturadas sob forma de uma hie

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Van, der Walt Christiaan Maarten. "Data measures that characterise classification problems." Diss., Pretoria : [s.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-08292008-162648/.

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Palm, Niklas. "Sentiment classification of Swedish Twitter data." Thesis, Uppsala universitet, Avdelningen för datalogi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388420.

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Sentiment analysis is a field within the area of natural language processing that studies the sentiment of human written text. Within sentiment analysis, sentiment classification is a research area that has been of growing interest since the advent of digital social-media platforms, concerned with the classification of the subjective information in text data. Many studies have been conducted on sentiment classification, producing numerous of openly available tools and resources that further advance research, though almost exclusively for the English language. There are very few openly availabl

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Tziatzios, Achilleas. "Data mining of range-based classification rules for data characterization." Thesis, Cardiff University, 2014. http://orca.cf.ac.uk/65902/.

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Advances in data gathering have led to the creation of very large collections across different fields like industrial site sensor measurements or the account statuses of a financial institution's clients. The ability to learn classification rules, rules that associate specific attribute values with a specific class label, from this data is important and useful in a range of applications. While many methods to facilitate this task have been proposed, existing work has focused on categorical datasets and very few solutions that can derive classification rules of associated continuous ranges (num

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Davari, Mahdad. "Advances Towards Data-Race-Free Cache Coherence Through Data Classification." Doctoral thesis, Uppsala universitet, Avdelningen för datorteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-320595.

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Providing a consistent view of the shared memory based on precise and well-defined semantics—memory consistency model—has been an enabling factor in the widespread acceptance and commercial success of shared-memory architectures. Moreover, cache coherence protocols have been employed by the hardware to remove from the programmers the burden of dealing with the memory inconsistency that emerges in the presence of the private caches. The principle behind all such cache coherence protocols is to guarantee that consistent values are read from the private caches at all times. In its most stringent

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Better, Marco L. "Data mining techniques for prediction and classification in discrete data applications." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3273688.

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Tennant, Mark. "A parallel data stream classification technique for high velocity data streams." Thesis, University of Reading, 2018. http://centaur.reading.ac.uk/77919/.

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Real-time classification of data streams remains one of the most challenging aspects of Big Data. As a data stream is an unending source of information, classification models and metrics must be created and adapted in real-time as the data is made available to them. This time constrained learning is problematic, conventional data models require a training period to examine the data and produce models for evaluation. In data stream mining this training period does not exist, instead the models are continuously updated in real-time. As data streams become faster and larger the quantity of data t

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Botella, Pérez Cristina. "Multivariate classification of gene expression microarray data." Doctoral thesis, Universitat Rovira i Virgili, 2010. http://hdl.handle.net/10803/9046.

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L'expressiódels gens obtinguts de l'anàliside microarrays s'utilitza en molts casos, per classificar les cèllules. En aquestatesi, unaversióprobabilística del mètodeDiscriminant Partial Least Squares (p-DPLS)s'utilitza per classificar les mostres de les expressions delsseus gens. p-DPLS esbasa en la regla de Bayes de la probabilitat a posteriori. Aquestsclassificadorssónforaçats a classficarsempre.Per superaraquestalimitaciós'haimplementatl'opció de rebuig.Aquestaopciópermetrebutjarlesmostresamb alt riscd'errors de classificació (és a dir, mostresambigüesi outliers).Aquestaopció de rebuigcombi

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22

Hajimohammadi, Hamid Reza. "Classification of Data Series at Vehicle Detection." Thesis, Uppsala University, Department of Information Technology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-111163.

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<p>This paper purposes a new, simple and lightweight approach of previously studied algorithms that can be used for extracting of feature vectors that in turn enables one to classify a vehicle based on its magnetic signature shape.This algorithm is called ASWA that stands for Adaptive Spectral and Wavelet Analysis and it is a combination of features of a signal extracted by both of the spectral and wavelet analysis algorithms. The performance of classifiers using this feature vectors is compared to another feature vectors consisting of features extracted by Fourier transform and pattern inform

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Selmer, Oyvind, and Mikael Brevik. "Classification and Visualisation of Twitter Sentiment Data." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-22967.

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The social micro-blog site Twitter grows in user base each day and has become an attractive platform for companies, politicians, marketeers, and others wishing to share information and/or opinions. With a growing user market for Twitter, more and more systems and research are released for taking advantage of its informal nature and doing opinion mining and sentiment analysis. This master thesis describes a system for doing Sentiment Analysis on Twitter data and experiments with grid searches on various combinations of machine learning algorithms, features and preprocessing methods to achieve s

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Gao, Ming. "A study on imbalanced data classification problems." Thesis, University of Reading, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.602707.

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This thesis focuses on the study of machine learning and pattern recognition algorithms for imbalanced data problems. The imbalanced problems are important as they are prevalent in life threatening/safety critical applications. They are known to be problematic to standard machine learning algorithms due to the imbalanced distribution between positive and negative classes. My original contribution to knowledge in this field is fourfold. A powerful and efficient algorithm for solving two-class imbalanced problems is proposed. The proposed method combines the synthetic minority over-sampling tech

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Acosta, Mena Dionisio M. "Statistical classification of magnetic resonance imaging data." Thesis, University of Sussex, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390913.

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Berry, Ian Michael. "Data classification using unsupervised artificial neural networks." Thesis, University of Sussex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390079.

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Hou, Jun. "Function Approximation and Classification with Perturbed Data." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1618266875924225.

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Al-Madi, Naila Shikri. "Improved Genetic Programming Techniques For Data Classification." Diss., North Dakota State University, 2014. https://hdl.handle.net/10365/27097.

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Evolutionary algorithms are one category of optimization techniques that are inspired by processes of biological evolution. Evolutionary computation is applied to many domains and one of the most important is data mining. Data mining is a relatively broad field that deals with the automatic knowledge discovery from databases and it is one of the most developed fields in the area of artificial intelligence. Classification is a data mining method that assigns items in a collection to target classes with the goal to accurately predict the target class for each item in the data. Genetic programmin

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Varnavas, Andreas Soteriou. "Signal processing methods for EEG data classification." Thesis, Imperial College London, 2008. http://hdl.handle.net/10044/1/11943.

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Hyun, Jung Kim. "Classification in thoracic computed tomography image data." Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1383469071&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Lee, K. K. "Classification of imbalanced data with transparent kernels." Thesis, University of Southampton, 2002. https://eprints.soton.ac.uk/257937/.

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Kazakeviciute, Agne. "Some theoretical essays on functional data classification." Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/1570359/.

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Functional data analysis is a fast-growing research area in statistics, dealing with statistical analysis of infinite-dimensional (functional) data. For many pattern recognition problems with finite-dimensional data there usually exists a solid theoretical foundation, for example, it is known under which assumptions various classifiers have desirable theoretical properties, such as consistency. Therefore, a natural interest is to extend the theory to the setting of infinite-dimensional data. The thesis is written in two directions: one is when we observe full curves, and the other is when we o

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DEMNI, Houyem. "Depth-based classification approaches for directional data." Doctoral thesis, Università degli studi di Cassino, 2021. http://hdl.handle.net/11580/83781.

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Supervised learning tasks aim to define a data-based rule by which new objects are assigned to one of the given classes. To this end, a training set containing objects with known memberships is exploited. Directional data are points lying on the surface of circles, spheres or hyper-spheres. Given that they lie on a non-linear manifold, directional observations require specific methods to be analyzed. In this thesis, the main interest is to present novel methodologies and to perform reliable inferences for directional data, within the framework of supervised classification. First, a supervised

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Palanisamy, Senthil Kumar. "Association rule based classification." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-050306-131517/.

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Thesis (M.S.)--Worcester Polytechnic Institute.<br>Keywords: Itemset Pruning, Association Rules, Adaptive Minimal Support, Associative Classification, Classification. Includes bibliographical references (p.70-74).

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Chan, Wing-yan Sarah, and 陳詠欣. "Emerging substrings for sequence classification." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B2971672X.

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Ramaboa, Kutlwano K. K. M. "A comparative evaluation of data mining classification techniques on medical trauma data." Master's thesis, University of Cape Town, 2004. http://hdl.handle.net/11427/5973.

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Includes bibliographical references (leaves 109-113).<br>The purpose of this research was to determine the extent to which a selection of data mining classification techniques (specifically, Discriminant Analysis, Decision Trees, and three artifical neural network models - Backpropogation, Probablilistic Neural Networks, and the Radial Basis Function) are able to correctly classify cases into the different categories of an outcome measure from a given set of input variables (i.e. estimate their classification accuracy) on a common database.

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Lundgren, Andreas. "Data-Driven Engine Fault Classification and Severity Estimation Using Residuals and Data." Thesis, Linköpings universitet, Fordonssystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165736.

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Recent technological advances in the automotive industry have made vehicularsystems increasingly complex in terms of both hardware and software. As thecomplexity of the systems increase, so does the complexity of efficient monitoringof these system. With increasing computational power the field of diagnosticsis becoming evermore focused on software solutions for detecting and classifyinganomalies in the supervised systems. Model-based methods utilize knowledgeabout the physical system to device nominal models of the system to detect deviations,while data-driven methods uses historical data to

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Pruengkarn, Ratchakoon. "Enhancing classification performance by handling noise and imbalanced data with fuzzy classification techniques." Thesis, Pruengkarn, Ratchakoon (2018) Enhancing classification performance by handling noise and imbalanced data with fuzzy classification techniques. PhD thesis, Murdoch University, 2018. https://researchrepository.murdoch.edu.au/id/eprint/42505/.

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This thesis studied the methodologies to improve the quality of training data in order to enhance classification performance. Noise and imbalance problems are two significant factors affecting data quality. Class noise is considered as the most harmful type of noise to a classifier’s performance, since incorrectly labelled examples may severely bias the learning method and result in inaccurate models. Removing mislabelled instances is more efficient than repairing and relabelling them. However, excessive removal of instances can be the cause of serious and irremediable loss of information. Und

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Klose, Aljoscha Alexander. "Partially supervised learning of fuzzy classification rules." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=971682364.

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Phillips, Rhonda D. "A Probabilistic Classification Algorithm With Soft Classification Output." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/26701.

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This thesis presents a shared memory parallel version of the hybrid classification algorithm IGSCR (iterative guided spectral class rejection), a novel data reduction technique that can be used in conjunction with PIGSCR (parallel IGSCR), a noise removal method based on the maximum noise fraction (MNF), and a continuous version of IGSCR (CIGSCR) that outputs soft classifications. All of the above are either classification algorithms or preprocessing algorithms necessary prior to the classification of high dimensional, noisy images. PIGSCR was developed to produce fast and portable code using

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Bressan, Marco José Miguel. "Statistical Independence for classification for High Dimensional Data." Doctoral thesis, Universitat Autònoma de Barcelona, 2003. http://hdl.handle.net/10803/3034.

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Röder, Tido. "Similarity, retrieval, and classification of motion capture data." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=983632332.

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Cho, Hansang. "Classification of functional brain data for multimedia retrieval /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/5892.

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Brandin, Martin, and Roger Hamrén. "Classification of Ground Objects Using Laser Radar Data." Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1572.

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<p>Accurate 3D models of natural environments are important for many modelling and simulation applications, for both civilian and military purposes. When building 3D models from high resolution data acquired by an airborne laser scanner it is de-sirable to separate and classify the data to be able to process it further. For example, to build a polygon model of a building the samples belonging to the building must be found.</p><p>In this thesis we have developed, implemented (in IDL and ENVI), and evaluated algorithms for classification of buildings, vegetation, power lines, posts, and roads. T

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Zhao, Lei. "Learning from noisy data: Robust data classification." Thesis, 2012. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/65174.

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The problem of learning from noisy data sets has been the focus of much attention for many years. Three different types of noise could be defined that generate difficulties in data classification. The first type is related to the noisy features and labels where data entry and data acquisition are inherently prone to errors. The second type is from the redundant features, which may confuse the classification algorithm and degrade the classification performance. The last type could be generated by insufficient features where some features may become quite ambiguous in the absence of related hidd

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Yu, Hsin-Min, and 余欣珉. "Applying Support Vector Data Description For Data Classification." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/61401033111710951818.

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碩士<br>朝陽科技大學<br>工業工程與管理系碩士班<br>101<br>Support Vector Data Description (SVDD) was developed by Tax and Duin in 1999. The objective of SVDD is to obtain a shaped decision boundary with minimum volume around a dataset. SVDD was firstly developed to detecting outliers. In this study, the SVDD will be adopted as a classification tool. The SVDD is unlimited to the data assumption. Moreover, the decision boundary is formed by Support Vectors (SVs) which are obtained from solving convex quadratic programming problem. This study aims at evaluating the impacts of preprocessing methods on the SVDD classi

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HO, MING-HSUAN, and 何明璇. "Classification of microarray data using fuzzy classification association rules." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/30548430326192789242.

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碩士<br>國立臺灣科技大學<br>資訊管理系<br>98<br>With the advent of microarray technology, people can now measure thousands of gene’s expressions simultaneously in one experiment. The powerful microarray technology helps lay the foundation for bioinformatics and is widely used in disease diagnosis. In this thesis, we use fuzzy classification association rules to study the relationship between gene expressions and diseases in microarray data. In the proposed method, we first divide the universe of discourse of each gene expression in microarray data into several intervals, and define a membership function for

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"Data Compression by Unsupervised Classification." Department of Statistics and Mathematics, 1997. http://epub.wu-wien.ac.at/dyn/dl/wp/epub-wu-01_a2f.

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Zhang, Xin. "Classification in the missing data." Master's thesis, 2010. http://hdl.handle.net/10048/1290.

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Missing data is always a problem when it comes to data analysis. This is especially the case in anthropology when sex determination is one of the primary goals for fossil skull data since many measurements were not available. We expect to find a classifier that can handle the large amount of missingness and improve the ability of prediction/classification as well. These are the objectives of this thesis. Besides of the crude methods (ignore cases with missingness), three possible techniques in handling of missing values are discussed: bootstrap imputation, weighted-averaging classifier and

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Yi, Jiang Jhih, and 姜芝怡. "An Incremental Data Classification Technique." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/05970856233052098455.

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碩士<br>國立清華大學<br>資訊系統與應用研究所<br>92<br>In this high competition age, a company has to continuously keep an eye on the latest information in order to hold the upper hand of the industry. The company may have to find the information on the mass media or on the market. They can even find useful information in their own database. The task of mining unseen information and then transforming it into the competitive strategy is essential in the data mining area. Customer relationship management system is one of the most popular data mining applications. In this study, we analyze a subsystem of a 3C re

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