Thematische Bibliographien / Multi-class classifiers

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Auswahl der wissenschaftlichen Literatur zum Thema „Multi-class classifiers“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 4. Februar 2022

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Zeitschriftenartikel zum Thema "Multi-class classifiers"

Bourke, Chris, Kun Deng, Stephen D. Scott, Robert E. Schapire, and N. V. Vinodchandran. "On reoptimizing multi-class classifiers." Machine Learning 71, no. 2-3 (2008): 219–42. http://dx.doi.org/10.1007/s10994-008-5056-8.

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Lin, Hung-Yi. "Efficient classifiers for multi-class classification problems." Decision Support Systems 53, no. 3 (2012): 473–81. http://dx.doi.org/10.1016/j.dss.2012.02.014.

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Siedlecki, Wojciech W. "A formula for multi-class distributed classifiers." Pattern Recognition Letters 15, no. 8 (1994): 739–42. http://dx.doi.org/10.1016/0167-8655(94)90001-9.

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Kang, Seokho, Sungzoon Cho, and Pilsung Kang. "Multi-class classification via heterogeneous ensemble of one-class classifiers." Engineering Applications of Artificial Intelligence 43 (August 2015): 35–43. http://dx.doi.org/10.1016/j.engappai.2015.04.003.

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Bo, Shukui, and Yongju Jing. "Data Distribution Partitioning for One-Class Extraction from Remote Sensing Imagery." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 09 (2017): 1754018. http://dx.doi.org/10.1142/s0218001417540180.

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One-class extraction from remotely sensed imagery is researched with multi-class classifiers in this paper. With two supervised multi-class classifiers, Bayesian classifier and nearest neighbor classifier, we firstly analyzed the effect of the data distribution partitioning on one-class extraction from the remote sensing images. The data distribution partitioning refers to the way that the data set is partitioned before classification. As a parametric method, the Bayesian classifier achieved good classification performance when the data distribution was partitioned appropriately. While as a nonparametric method, the NN classifier did not require a detailed partitioning of the data distribution. For simplicity, the data set can be partitioned into two classes, the class of interest and the remainder, to extract the specific class. With appropriate partitioning of the data set, the specific class of interest was well extracted from remotely sensed imagery in the experiments. This study will be helpful for one-class extraction from remote sensing imagery with multi-class classifiers. It provides a way to improve the one-class classification from the aspect of data distribution partitioning.

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Liu, Jinfu, Mingliang Bai, Na Jiang, et al. "Interclass Interference Suppression in Multi-Class Problems." Applied Sciences 11, no. 1 (2021): 450. http://dx.doi.org/10.3390/app11010450.

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Multi-classifiers are widely applied in many practical problems. But the features that can significantly discriminate a certain class from others are often deleted in the feature selection process of multi-classifiers, which seriously decreases the generalization ability. This paper refers to this phenomenon as interclass interference in multi-class problems and analyzes its reason in detail. Then, this paper summarizes three interclass interference suppression methods including the method based on all-features, one-class classifiers and binary classifiers and compares their effects on interclass interference via the 10-fold cross-validation experiments in 14 UCI datasets. Experiments show that the method based on binary classifiers can suppress the interclass interference efficiently and obtain the best classification accuracy among the three methods. Further experiments were done to compare the suppression effect of two methods based on binary classifiers including the one-versus-one method and one-versus-all method. Results show that the one-versus-one method can obtain a better suppression effect on interclass interference and obtain better classification accuracy. By proposing the concept of interclass inference and studying its suppression methods, this paper significantly improves the generalization ability of multi-classifiers.

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Maximov, Yu, and D. Reshetova. "Tight risk bounds for multi-class margin classifiers." Pattern Recognition and Image Analysis 26, no. 4 (2016): 673–80. http://dx.doi.org/10.1134/s105466181604009x.

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D’Andrea, Eleonora, and Beatrice Lazzerini. "A hierarchical approach to multi-class fuzzy classifiers." Expert Systems with Applications 40, no. 9 (2013): 3828–40. http://dx.doi.org/10.1016/j.eswa.2012.12.097.

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Abdallah, Loai, Murad Badarna, Waleed Khalifa, and Malik Yousef. "MultiKOC: Multi-One-Class Classifier Based K-Means Clustering." Algorithms 14, no. 5 (2021): 134. http://dx.doi.org/10.3390/a14050134.

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In the computational biology community there are many biological cases that are considered as multi-one-class classification problems. Examples include the classification of multiple tumor types, protein fold recognition and the molecular classification of multiple cancer types. In all of these cases the real world appropriately characterized negative cases or outliers are impractical to achieve and the positive cases might consist of different clusters, which in turn might lead to accuracy degradation. In this paper we present a novel algorithm named MultiKOC multi-one-class classifiers based K-means to deal with this problem. The main idea is to execute a clustering algorithm over the positive samples to capture the hidden subdata of the given positive data, and then building up a one-class classifier for every cluster member’s examples separately: in other word, train the OC classifier on each piece of subdata. For a given new sample, the generated classifiers are applied. If it is rejected by all of those classifiers, the given sample is considered as a negative sample, otherwise it is a positive sample. The results of MultiKOC are compared with the traditional one-class, multi-one-class, ensemble one-classes and two-class methods, yielding a significant improvement over the one-class and like the two-class performance.

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Krawczyk, Bartosz, Mikel Galar, Michał Woźniak, Humberto Bustince, and Francisco Herrera. "Dynamic ensemble selection for multi-class classification with one-class classifiers." Pattern Recognition 83 (November 2018): 34–51. http://dx.doi.org/10.1016/j.patcog.2018.05.015.

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Dissertationen zum Thema "Multi-class classifiers"

Kybartas, Rimantas. "Multi-class recognition using pair-wise classifiers." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20101001_150424-92661.

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There are plenty of solutions for the task of multi-class recognition. Unfortunately, these solutions are not always unanimous. Most of them are based on empirical experiments while statistical data features consideration is often omitted. That’s why questions like when and which method should be used, what the reliability of any chosen method is for solving a multi-class recognition task arise. In this dissertation two-stage multi-class decision methods are analyzed. Pair-wise classifiers able to better exploit statistical data features are used in the first stage of such methods. In the second stage a particular fusion rule of the first stage results is used to fuse the first stage results in order to produce the final classification decision. Complexity issues of pair-wise classifiers, training data size and precision of method quality estimation are pointed out in the research. The precision of algorithm highly depends on the data and the number of experiments performed (data permutation, division into training and testing data). It is shown that the declared superiority of some known algorithms is not reliable due to low precision of estimation. A detailed comparison of well known multi-class classification methods is performed and a new pair-wise classifier fusion method based on similar method used in multi-class classifier fusion is presented. The recommendations for multi-class classification task designer are provided. Methods which allow reducing classification... [to full text] Daugelio klasių atpažinimo uždaviniams spręsti yra sukurta aibė sprendimų ir ne visada vieningų rekomendacijų. Dauguma jų paremta empiriniais bandymais, retai atsižvelgiama į statistines duomenų savybes. Dėl to sprendžiant daugelio klasių klasifikavimo uždavinį kyla klausimų, kurį metodą ir kada geriausia naudoti, koks vieno ar kito metodo patikimumas. Disertacijoje nagrinėjami dviejų pakopų sprendimo priėmimo metodai, kai pirmame etape sudaromi klasifikatoriai poroms (angl. pair-wise), sugebantys geriau išnaudoti klasių tarpusavio statistines savybes, o kitame etape yra atliekamas klasifikatorių poroms rezultatų apjungimas. Tyrime ypatingas dėmesys yra skiriamas klasifikatorių poroms sudėtingumui, mokymo duomenų kiekiui bei algoritmų kokybės įvertinimo tikslumui. Tikslumas labai priklauso nuo duomenų bei atliktų eksperimentų kiekio (duomenų permaišymo klasėse, juos skirstant į mokymo ir testavimo). Parodyta, jog dėl žemo įvertinimo tikslumo kai kurių publikuotų algoritmų deklaruojamas pranašumas prieš žinomus algoritmus nėra patikimas. Darbe atliktas detalus žinomų metodų palyginimas bei pristatytas naujai sukurtas klasifikatorių poroms apjungimo algoritmas, kuris yra paremtas analogišku algoritmu daugelio klasių klasifikatorių rezultatų apjungimui. Pateiktos bendros rekomendacijos, kaip projektuotojui elgtis daugelio klasių atveju. Pasiūlyti metodai, leidžiantys sumažinti klasifikavimo klaidą atliekant klasifikatorių poroms apjungimo koregavimą, kad algoritmas nebūtų... [toliau žr. visą tekstą]

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Abd, Rahman Mohd Amiruddin. "Kernel and multi-class classifiers for multi-floor WLAN localisation." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/13768/.

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Indoor localisation techniques in multi-floor environments are emerging for location based service applications. Developing an accurate location determination and time-efficient technique is crucial for online location estimation of the multi-floor localisation system. The localisation accuracy and computational complexity of the localisation system mainly relies on the performance of the algorithms embedded with the system. Unfortunately, existing algorithms are either time-consuming or inaccurate for simultaneous determination of floor and horizontal locations in multi-floor environment. This thesis proposes an improved multi-floor localisation technique by integrating three important elements of the system; radio map fingerprint database optimisation, floor or vertical localisation, and horizontal localisation. The main focus of this work is to extend the kernel density approach and implement multi-class machine learning classifiers to improve the localisation accuracy and processing time of the each and overall elements of the proposed technique. For fingerprint database optimisation, novel access point (AP) selection algorithms which are based on variant AP selection are investigated to improve computational accuracy compared to existing AP selection algorithms such as Max-Mean and InfoGain. The variant AP selection is further improved by grouping AP based on signal distribution. In this work, two AP selection algorithms are proposed which are Max Kernel and Kernel Logistic Discriminant that implement the knowledge of kernel density estimate and logistic regression machine learning classification. For floor localisation, the strategy is based on developing the algorithm to determine the floor by utilising fingerprint clustering technique. The clustering method is based on simple signal strength clustering which sorts the signals of APs in each fingerprint according to the strongest value. Two new floor localisation algorithms namely Averaged Kernel Floor (AKF) and Kernel Logistic Floor (KLF) are studied. The former is based on modification of univariate kernel algorithm which is proposed for single-floor localisation, while the latter applies the theory kernel logistic regression which is similar to AP selection approach but for classification purpose. For horizontal localisation, different algorithm based on multi-class k-nearest neighbour ( NN) classifiers with optimisation parameter is presented. Unlike the classical kNN algorithm which is a regression type algorithm, the proposed localisation algorithms utilise machine learning classification for both linear and kernel types. The multi-class classification strategy is used to ensure quick estimation of the multi-class NN algorithms. The proposed algorithms are compared and analysed with existing algorithms to confirm reliability and robustness. Additionally, the algorithms are evaluated using six multi-floor and single-floor datasets to validate the proposed algorithms. In database optimisation, the proposed AP selection technique using Max Kernel could reduce as high as 77.8% APs compared to existing approaches while retaining similar accuracy as localisation algorithm utilising all APs in the database. In floor localisation, the proposed KLF algorithm at one time could demonstrate 93.4% correct determination of floor level based on the measured dataset. In horizontal localisation, the multi-class NN classifier algorithm could improve 19.3% of accuracy within fingerprint spacing of 2 meters compared to existing algorithms. All of the algorithms are later combined to provide device location estimation for multi-floor environment. Improvement of 43.5% of within 2 meters location accuracy and reduction of 15.2 times computational time are seen as compared to existing multi-floor localisation techniques by Gansemer and Marques. The improved accuracy is due to better performance of proposed floor and horizontal localisation algorithm while the computational time is reduced due to introduction of AP selection algorithm.

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Beneš, Jiří. "Unární klasifikátor obrazových dat." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442432.

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The work deals with an introduction to classification algorithms. It then divides classifiers into unary, binary and multi-class and describes the different types of classifiers. The work compares individual classifiers and their areas of use. For unary classifiers, practical examples and a list of used architectures are given in the work. The work contains a chapter focused on the comparison of the effects of hyper parameters on the quality of unary classification for individual architectures. Part of the submission is a practical example of reimplementation of the unary classifier.

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Odabai, Fard Seyed Hamidreza. "Efficient multi-class objet detection with a hierarchy of classes." Thesis, Clermont-Ferrand 2, 2015. http://www.theses.fr/2015CLF22623/document.

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Dans cet article, nous présentons une nouvelle approche de détection multi-classes basée sur un parcours hiérarchique de classifieurs appris simultanément. Pour plus de robustesse et de rapidité, nous proposons d’utiliser un arbre de classes d’objets. Notre modèle de détection est appris en combinant les contraintes de tri et de classification dans un seul problème d’optimisation. Notre formulation convexe permet d’utiliser un algorithme de recherche pour accélérer le temps d’exécution. Nous avons mené des évaluations de notre algorithme sur les benchmarks PASCAL VOC (2007 et 2010). Comparé à l’approche un-contre-tous, notre méthode améliore les performances pour 20 classes et gagne 10x en vitesse Recent years have witnessed a competition in autonomous navigation for vehicles boosted by the advances in computer vision. The on-board cameras are capable of understanding the semantic content of the environment. A core component of this system is to localize and classify objects in urban scenes. There is a need to have multi-class object detection systems. Designing such an efficient system is a challenging and active research area. The algorithms can be found for applications in autonomous driving, object searches in images or video surveillance. The scale of object classes varies depending on the tasks. The datasets for object detection started with containing one class only e.g. the popular INRIA Person dataset. Nowadays, we witness an expansion of the datasets consisting of more training data or number of object classes. This thesis proposes a solution to efficiently learn a multi-class object detector. The task of such a system is to localize all instances of target object classes in an input image. We distinguish between three major efficiency criteria. First, the detection performance measures the accuracy of detection. Second, we strive low execution times during run-time. Third, we address the scalability of our novel detection framework. The two previous criteria should scale suitably with the number of input classes and the training algorithm has to take a reasonable amount of time when learning with these larger datasets. Although single-class object detection has seen a considerable improvement over the years, it still remains a challenge to create algorithms that work well with any number of classes. Most works on this subject extent these single-class detectors to work accordingly with multiple classes but remain hardly flexible to new object descriptors. Moreover, they do not consider all these three criteria at the same time. Others use a more traditional approach by iteratively executing a single-class detector for each target class which scales linearly in training time and run-time. To tackle the challenges, we present a novel framework where for an input patch during detection the closest class is ranked highest. Background labels are rejected as negative samples. The detection goal is to find the highest scoring class. To this end, we derive a convex problem formulation that combines ranking and classification constraints. The accuracy of the system is improved by hierarchically arranging the classes into a tree of classifiers. The leaf nodes represent the individual classes and the intermediate nodes called super-classes group recursively these classes together. The super-classes benefit from the shared knowledge of their descending classes. All these classifiers are learned in a joint optimization problem along with the previouslymentioned constraints. The increased number of classifiers are prohibitive to rapid execution times. The formulation of the detection goal naturally allows to use an adapted tree traversal algorithm to progressively search for the best class but reject early in the detection process the background samples and consequently reduce the system’s run-time. Our system balances between detection performance and speed-up. We further experimented with feature reduction to decrease the overhead of applying the high-level classifiers in the tree. The framework is transparent to the used object descriptor where we implemented the histogram of orientated gradients and deformable part model both introduced in [Felzenszwalb et al., 2010a]. The capabilities of our system are demonstrated on two challenging datasets containing different object categories not necessarily semantically related. We evaluate both the detection performance with different number of classes and the scalability with respect to run-time. Our experiments show that this framework fulfills the requirements of a multi-class object detector and highlights the advantages of structuring class-level knowledge

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Verschae, Tannenbaum Rodrigo. "Object Detection Using Nested Cascades of Boosted Classifiers. A Learning Framework and Its Extension to The Multi-Class Case." Tesis, Universidad de Chile, 2010. http://www.repositorio.uchile.cl/handle/2250/102398.

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Mauricio-Sanchez, David, Andrade Lopes Alneu de, and higuihara Juarez Pedro Nelson. "Approaches based on tree-structures classifiers to protein fold prediction." Institute of Electrical and Electronics Engineers Inc, 2017. http://hdl.handle.net/10757/622536.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. Protein fold recognition is an important task in the biological area. Different machine learning methods such as multiclass classifiers, one-vs-all and ensemble nested dichotomies were applied to this task and, in most of the cases, multiclass approaches were used. In this paper, we compare classifiers organized in tree structures to classify folds. We used a benchmark dataset containing 125 features to predict folds, comparing different supervised methods and achieving 54% of accuracy. An approach related to tree-structure of classifiers obtained better results in comparison with a hierarchical approach. Revisión por pares

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Abdelhamid, Neda. "Deriving classifiers with single and multi-label rules using new Associative Classification methods." Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/10120.

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Associative Classification (AC) in data mining is a rule based approach that uses association rule techniques to construct accurate classification systems (classifiers). The majority of existing AC algorithms extract one class per rule and ignore other class labels even when they have large data representation. Thus, extending current AC algorithms to find and extract multi-label rules is promising research direction since new hidden knowledge is revealed for decision makers. Furthermore, the exponential growth of rules in AC has been investigated in this thesis aiming to minimise the number of candidate rules, and therefore reducing the classifier size so end-user can easily exploit and maintain it. Moreover, an investigation to both rule ranking and test data classification steps have been conducted in order to improve the performance of AC algorithms in regards to predictive accuracy. Overall, this thesis investigates different problems related to AC not limited to the ones listed above, and the results are new AC algorithms that devise single and multi-label rules from different applications data sets, together with comprehensive experimental results. To be exact, the first algorithm proposed named Multi-class Associative Classifier (MAC): This algorithm derives classifiers where each rule is connected with a single class from a training data set. MAC enhanced the rule discovery, rule ranking, rule filtering and classification of test data in AC. The second algorithm proposed is called Multi-label Classifier based Associative Classification (MCAC) that adds on MAC a novel rule discovery method which discovers multi-label rules from single label data without learning from parts of the training data set. These rules denote vital information ignored by most current AC algorithms which benefit both the end-user and the classifier's predictive accuracy. Lastly, the vital problem related to web threats called 'website phishing detection' was deeply investigated where a technical solution based on AC has been introduced in Chapter 6. Particularly, we were able to detect new type of knowledge and enhance the detection rate with respect to error rate using our proposed algorithms and against a large collected phishing data set. Thorough experimental tests utilising large numbers of University of California Irvine (UCI) data sets and a variety of real application data collections related to website classification and trainer timetabling problems reveal that MAC and MCAC generates better quality classifiers if compared with other AC and rule based algorithms with respect to various evaluation measures, i.e. error rate, Label-Weight, Any-Label, number of rules, etc. This is mainly due to the different improvements related to rule discovery, rule filtering, rule sorting, classification step, and more importantly the new type of knowledge associated with the proposed algorithms. Most chapters in this thesis have been disseminated or under review in journals and refereed conference proceedings.

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Son, Kyung-Im. "A multi-class, multi-dimensional classifier as a topology selector for analog circuit design / by Kyung-Im Son." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/5919.

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Bautista, Martín Miguel Ángel. "Learning error-correcting representations for multi-class problems." Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/396124.

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Real life is full of multi-class decision tasks. In the Pattern Recognition ﬁeld, several method- ologies have been proposed to deal with binary problems obtaining satisfying results in terms of performance. However, the extension of very powerful binary classiﬁers to the multi-class case is a complex task. The Error-Correcting Output Codes framework has demonstrated to be a very powerful tool to combine binary classiﬁers to tackle multi-class problems. However, most of the combinations of binary classiﬁers in the ECOC framework overlook the underlay- ing structure of the multi-class problem. In addition, is still unclear how the Error-Correction of an ECOC design is distributed among the diﬀerent classes. In this dissertation, we are interested in tackling critic problems of the ECOC framework, such as the deﬁnition of the number of classiﬁers to tackle a multi-class problem, how to adapt the ECOC coding to multi-class data and how to distribute error-correction among diﬀerent pairs of categories. In order to deal with this issues, this dissertation describes several proposals. 1) We deﬁne a new representation for ECOC coding matrices that expresses the pair-wise codeword separability and allows for a deeper understanding of how error-correction is distributed among classes. 2) We study the eﬀect of using a logarithmic number of binary classiﬁers to treat the multi-class problem in order to obtain very eﬃcient models. 3) In order to search for very compact ECOC coding matrices that take into account the distribution of multi-class data we use Genetic Algorithms that take into account the constraints of the ECOC framework. 4) We propose a discrete factorization algorithm that ﬁnds an ECOC conﬁguration that allocates the error-correcting capabilities to those classes that are more prone to errors. The proposed methodologies are evaluated on diﬀerent real and synthetic data sets: UCI Machine Learning Repository, handwriting symbols, traﬃc signs from a Mobile Mapping System, and Human Pose Recovery. The results of this thesis show that signiﬁcant perfor- mance improvements are obtained on traditional coding ECOC designs when the proposed ECOC coding designs are taken into account. En la vida cotidiana las tareas de decisión multi-clase surgen constantemente. En el campo de Reconocimiento de Patrones muchos métodos de clasificación binaria han sido propuestos obteniendo resultados altamente satisfactorios en términos de rendimiento. Sin embargo, la extensión de estos sofisticados clasificadores binarios al contexto multi-clase es una tarea compleja. En este ámbito, las estrategias de Códigos Correctores de Errores (CCEs) han demostrado ser una herramienta muy potente para tratar la combinación de clasificadores binarios. No obstante, la mayoría de arquitecturas de combinación de clasificadores binarios negligen la estructura del problema multi-clase. Sin embargo, el análisis de la distribución de corrección de errores entre clases es aún un problema abierto. En esta tesis doctoral, nos centramos en tratar problemas críticos de los códigos correctores de errores; la definición del número de clasificadores necesarios para tratar un problema multi-clase arbitrario; la adaptación de los problemas binarios al problema multi-clase y cómo distribuir la corrección de errores entre clases. Para dar respuesta a estas cuestiones, en esta tesis doctoral describimos varias propuestas. 1) Definimos una nueva representación para CCEs que expresa la separabilidad entre pares de códigos y nos permite una mejor comprensión de cómo se distribuye la corrección de errores entre distintas clases. 2) Estudiamos el efecto de usar un número logarítmico de clasificadores binarios para tratar el problema multi-clase con el objetivo de obtener modelos muy eficientes. 3) Con el objetivo de encontrar modelos muy eficientes que tienen en cuenta la estructura del problema multi-clase utilizamos algoritmos genéticos que tienen en cuenta las restricciones de los ECCs. 4) Pro- ponemos un algoritmo de factorización de matrices discreta que encuentra ECCs con una configuración que distribuye corrección de error a aquellas categorías que son más propensas a tener errores. Las metodologías propuestas son evaluadas en distintos problemas reales y sintéticos como por ejemplo: Repositorio UCI de Aprendizaje Automático, reconocimiento de símbolos escritos, clasificación de señales de tráfico y reconocimiento de la pose humana. Los resultados obtenidos en esta tesis muestran mejoras significativas en rendimiento comparados con los diseños tradiciones de ECCs cuando las distintas propuestas se tienen en cuenta.

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Rocha, Anderson de Rezende 1980. "Classificadores e aprendizado em processamento de imagens e visão computacional." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/276019.

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Orientador: Siome Klein Goldenstein Tese (doutorado) - Universidade Estadual de Campinas, Instituto da Computação Made available in DSpace on 2018-08-12T17:37:15Z (GMT). No. of bitstreams: 1 Rocha_AndersondeRezende_D.pdf: 10303487 bytes, checksum: 243dccfe5255c828ce7ead27c27eb1cd (MD5) Previous issue date: 2009 Resumo: Neste trabalho de doutorado, propomos a utilizaçãoo de classificadores e técnicas de aprendizado de maquina para extrair informações relevantes de um conjunto de dados (e.g., imagens) para solução de alguns problemas em Processamento de Imagens e Visão Computacional. Os problemas de nosso interesse são: categorização de imagens em duas ou mais classes, detecçãao de mensagens escondidas, distinção entre imagens digitalmente adulteradas e imagens naturais, autenticação, multi-classificação, entre outros. Inicialmente, apresentamos uma revisão comparativa e crítica do estado da arte em análise forense de imagens e detecção de mensagens escondidas em imagens. Nosso objetivo é mostrar as potencialidades das técnicas existentes e, mais importante, apontar suas limitações. Com esse estudo, mostramos que boa parte dos problemas nessa área apontam para dois pontos em comum: a seleção de características e as técnicas de aprendizado a serem utilizadas. Nesse estudo, também discutimos questões legais associadas a análise forense de imagens como, por exemplo, o uso de fotografias digitais por criminosos. Em seguida, introduzimos uma técnica para análise forense de imagens testada no contexto de detecção de mensagens escondidas e de classificação geral de imagens em categorias como indoors, outdoors, geradas em computador e obras de arte. Ao estudarmos esse problema de multi-classificação, surgem algumas questões: como resolver um problema multi-classe de modo a poder combinar, por exemplo, caracteríisticas de classificação de imagens baseadas em cor, textura, forma e silhueta, sem nos preocuparmos demasiadamente em como normalizar o vetor-comum de caracteristicas gerado? Como utilizar diversos classificadores diferentes, cada um, especializado e melhor configurado para um conjunto de caracteristicas ou classes em confusão? Nesse sentido, apresentamos, uma tecnica para fusão de classificadores e caracteristicas no cenário multi-classe através da combinação de classificadores binários. Nós validamos nossa abordagem numa aplicação real para classificação automática de frutas e legumes. Finalmente, nos deparamos com mais um problema interessante: como tornar a utilização de poderosos classificadores binarios no contexto multi-classe mais eficiente e eficaz? Assim, introduzimos uma tecnica para combinação de classificadores binarios (chamados classificadores base) para a resolução de problemas no contexto geral de multi-classificação. Abstract: In this work, we propose the use of classifiers and machine learning techniques to extract useful information from data sets (e.g., images) to solve important problems in Image Processing and Computer Vision. We are particularly interested in: two and multi-class image categorization, hidden messages detection, discrimination among natural and forged images, authentication, and multiclassification. To start with, we present a comparative survey of the state-of-the-art in digital image forensics as well as hidden messages detection. Our objective is to show the importance of the existing solutions and discuss their limitations. In this study, we show that most of these techniques strive to solve two common problems in Machine Learning: the feature selection and the classification techniques to be used. Furthermore, we discuss the legal and ethical aspects of image forensics analysis, such as, the use of digital images by criminals. We introduce a technique for image forensics analysis in the context of hidden messages detection and image classification in categories such as indoors, outdoors, computer generated, and art works. From this multi-class classification, we found some important questions: how to solve a multi-class problem in order to combine, for instance, several different features such as color, texture, shape, and silhouette without worrying about the pre-processing and normalization of the combined feature vector? How to take advantage of different classifiers, each one custom tailored to a specific set of classes in confusion? To cope with most of these problems, we present a feature and classifier fusion technique based on combinations of binary classifiers. We validate our solution with a real application for automatic produce classification. Finally, we address another interesting problem: how to combine powerful binary classifiers in the multi-class scenario more effectively? How to boost their efficiency? In this context, we present a solution that boosts the efficiency and effectiveness of multi-class from binary techniques. Doutorado Engenharia de Computação Doutor em Ciência da Computação

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Buchteile zum Thema "Multi-class classifiers"

Xiao, Han, Thomas Stibor, and Claudia Eckert. "Evasion Attack of Multi-class Linear Classifiers." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30217-6_18.

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Roth, Volker. "Probabilistic Discriminative Kernel Classifiers for Multi-Class Problems." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45404-7_33.

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Hadjadji, Bilal, Youcef Chibani, and Hassiba Nemmour. "Fuzzy Integral Combination of One-Class Classifiers Designed for Multi-class Classification." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11758-4_35.

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Raudys, Sarunas, Vitalij Denisov, and Antanas Andrius Bielskis. "A Pool of Classifiers by SLP: A Multi-class Case." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11867661_5.

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San, Cho Cho, Mie Mie Su Thwin, and Naing Linn Htun. "Malicious Software Family Classification using Machine Learning Multi-class Classifiers." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2622-6_41.

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Schirra, Lyn-Rouven, Florian Schmid, Hans A. Kestler, and Ludwig Lausser. "Interpretable Classifiers in Precision Medicine: Feature Selection and Multi-class Categorization." In Artificial Neural Networks in Pattern Recognition. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46182-3_9.

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Takenouchi, Takashi, and Shin Ishii. "A Unified Framework of Binary Classifiers Ensemble for Multi-class Classification." In Neural Information Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34481-7_46.

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Hirasawa, Shigeichi, Gendo Kumoi, Manabu Kobayashi, Masayuki Goto, and Hiroshige Inazumi. "System Evaluation of Construction Methods for Multi-class Problems Using Binary Classifiers." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77712-2_86.

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Yukinawa, Naoto, Shigeyuki Oba, Kikuya Kato, and Shin Ishii. "Multi-class Pattern Classification Based on a Probabilistic Model of Combining Binary Classifiers." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11550907_54.

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Theissler, Andreas, Simon Vollert, Patrick Benz, Laurentius A. Meerhoff, and Marc Fernandes. "ML-ModelExplorer: An Explorative Model-Agnostic Approach to Evaluate and Compare Multi-class Classifiers." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57321-8_16.

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Konferenzberichte zum Thema "Multi-class classifiers"

Xu, Jie, Xianglong Liu, Zhouyuan Huo, Cheng Deng, Feiping Nie, and Heng Huang. "Multi-Class Support Vector Machine via Maximizing Multi-Class Margins." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/440.

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Support Vector Machine (SVM) is originally proposed as a binary classification model, and it has already achieved great success in different applications. In reality, it is more often to solve a problem which has more than two classes. So, it is natural to extend SVM to a multi-class classifier. There have been many works proposed to construct a multi-class classifier based on binary SVM, such as one versus all strategy, one versus one strategy and Weston's multi-class SVM. One versus all strategy and one versus one strategy split the multi-class problem to multiple binary classification subproblems, and we need to train multiple binary classifiers. Weston's multi-class SVM is formed by ensuring risk constraints and imposing a specific regularization, like Frobenius norm. It is not derived by maximizing the margin between hyperplane and training data which is the motivation in SVM. In this paper, we propose a multi-class SVM model from the perspective of maximizing margin between training points and hyperplane, and analyze the relation between our model and other related methods. In the experiment, it shows that our model can get better or compared results when comparing with other related methods.

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Gautam, Chandan, Aruna Tiwari, and Sriram Ravindran. "Construction of multi-class classifiers by Extreme Learning Machine based one-class classifiers." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727445.

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Tao Ban and S. Abe. "Implementing Multi-class Classifiers by One-class Classification Methods." In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.246699.

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Danielsson, Oscar, and Stefan Carlsson. "Projectable classifiers for multi-view object class recognition." In 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). IEEE, 2011. http://dx.doi.org/10.1109/iccvw.2011.6130295.

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Mai, Tien-Dung, Thanh Duc Ngo, Duy-Dinh Le, Duc Anh Duong, Kiem Hoang, and Shin'ichi Satoh. "Large scale multi-class classification using latent classifiers." In 2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2015. http://dx.doi.org/10.1109/mmsp.2015.7340800.

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Kumar, Himanshu, and P. S. Sastry. "Robust Loss Functions for Learning Multi-class Classifiers." In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2018. http://dx.doi.org/10.1109/smc.2018.00125.

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Traganitis, Panagiotis A., and Georgios B. Giannakis. "Blind Multi-class Ensemble Learning with Dependent Classifiers." In 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. http://dx.doi.org/10.23919/eusipco.2018.8553113.

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Mangalkar, Priyanka, and Vaishali Barkade. "Efficient Categorization of Document with J48 Multi-Class Classifiers." In 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA). IEEE, 2019. http://dx.doi.org/10.1109/iccubea47591.2019.9128665.

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De-Qiang Han, Chong-Zhao Han, and Yi Yang. "Multi-class SVM classifiers fusion based on evidence combination." In International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR '07. IEEE, 2007. http://dx.doi.org/10.1109/icwapr.2007.4420736.

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Beekhof, Fokko, Sviatoslav Voloshynovskiy, Oleksiy Koval, and Taras Holotyak. "Multi-class classifiers based on binary classifiers: Performance, efficiency, and minimum coding matrix distances." In 2009 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2009. http://dx.doi.org/10.1109/mlsp.2009.5306199.

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