Journal articles: 'Classification binaire supervisée'

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Chehata, Nesrine, Karim Ghariani, Arnaud Le Bris, and Philippe Lagacherie. "Apport des images pléiades pour la délimitation des parcelles agricoles à grande échelle." Revue Française de Photogrammétrie et de Télédétection, no. 209 (January 29, 2015): 165–71. http://dx.doi.org/10.52638/rfpt.2015.220.

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Les pratiques et les arrangements spatiaux des parcelles agricoles ont un fort impact sur les flux d'eau dans les paysages cultivés . Afin de surveiller les paysages à grande échelle, il ya un fort besoin de délimitation automatique ou semi-automatique des parcelles agricoles. Cet article montre la contribution des images satellitaires à très haute résolution spatiales, telles que Pléiades, pour délimiter le parcellaire agricole de manière automatique .On propose une approche originale utilisant une classification binaire supervisée des limites. Une approche d'apprentissage actif est proposée afin d'adapter le modèle de classifieur au contexte local permettant ainsi la délimitation parcellaire à grande échelle.Le classifieur des Forêts Aléatoires est utilisé pour la classification et la sélection des attributs . Le concept de marge non supervisée est utilisé comme mesure d'incertitude dans l'algorithme d'apprentissage actif. En outre, un étiquetage automatique des pixels incertains est proposé en utilisant une approche hybride qui combinant une approche région et le concept de marge.Des résultats satisfaisants sont obtenus sur une image Pléiades. Différentes stratégies d'apprentissage sont comparées et discutées . Pour un cas d'étude opérationnel, un modèle global ou bien un modèle simple enrichi peuvent être utilisés en fonction des données de terrain disponibles.

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Stausberg, J., and D. Nasseh. "Evaluation of a Binary Semi-supervised Classification Technique for Probabilistic Record Linkage." Methods of Information in Medicine 55, no. 02 (2016): 136–43. http://dx.doi.org/10.3414/me14-01-0087.

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SummaryBackground: The process of merging data of different data sources is referred to as record linkage. A medical environment with increased preconditions on privacy protection demands the transformation of clear-text attributes like first name or date of birth into one-way encrypted pseudonyms. When performing an automated or privacy preserving record linkage there might be the need of a binary classification deciding whether two records should be classified as the same entity. The classification is the final of the four main phases of the record linkage process: Preprocessing, indexing, matching and classification. The choice of binary classification techniques in dependence of project specifications in particular data quality has not extensively been studied yet.Objectives: The aim of this work is the introduction and evaluation of an automatable semi-supervised binary classification system applied within the field of record linkage capable of competing or even surpassing advanced automated techniques of the domain of unsupervised classification.Methods: This work describes the rationale leading to the model and the final implementation of an automatable semi-supervised binary classification system and the comparison of its classification performance to an advanced active learning approach out of the domain of unsupervised learning. The performance of both systems has been measured on a broad variety of artificial test sets (n = 400), based on real patient data, with distinct and unique characteristics.Results: While the classification performance for both methods measured as F-measure was relatively close on test sets with maximum defined data quality, 0.996 for semi-supervised classification, 0.993 for unsupervised classification, it incrementally diverged for test sets of worse data quality dropping to 0.964 for semi-supervised classification and 0.803 for unsupervised classification.Conclusions: Aside from supplying a viable model for semi-supervised classification for automated probabilistic record linkage, the tests conducted on a large amount of test sets suggest that semi-supervised techniques might generally be capable of outperforming unsupervised techniques especially on data with lower levels of data quality.

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Arnason, R. M., P. Barmby, and N. Vulic. "Identifying new X-ray binary candidates in M31 using random forest classification." Monthly Notices of the Royal Astronomical Society 492, no. 4 (February 3, 2020): 5075–88. http://dx.doi.org/10.1093/mnras/staa207.

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ABSTRACT Identifying X-ray binary (XRB) candidates in nearby galaxies requires distinguishing them from possible contaminants including foreground stars and background active galactic nuclei. This work investigates the use of supervised machine learning algorithms to identify high-probability XRB candidates. Using a catalogue of 943 Chandra X-ray sources in the Andromeda galaxy, we trained and tested several classification algorithms using the X-ray properties of 163 sources with previously known types. Amongst the algorithms tested, we find that random forest classifiers give the best performance and work better in a binary classification (XRB/non-XRB) context compared to the use of multiple classes. Evaluating our method by comparing with classifications from visible-light and hard X-ray observations as part of the Panchromatic Hubble Andromeda Treasury, we find compatibility at the 90 per cent level, although we caution that the number of source in common is rather small. The estimated probability that an object is an XRB agrees well between the random forest binary and multiclass approaches and we find that the classifications with the highest confidence are in the XRB class. The most discriminating X-ray bands for classification are the 1.7–2.8, 0.5–1.0, 2.0–4.0, and 2.0–7.0 keV photon flux ratios. Of the 780 unclassified sources in the Andromeda catalogue, we identify 16 new high-probability XRB candidates and tabulate their properties for follow-up.

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Hung, Cheng-An, and Sheng-Fuu Lin. "Supervised Adaptive Hamming Net for Classification of Multiple-Valued Patterns." International Journal of Neural Systems 08, no. 02 (April 1997): 181–200. http://dx.doi.org/10.1142/s0129065797000203.

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A Supervised Adaptive Hamming Net (SAHN) is introduced for incremental learning of recognition categories in response to arbitrary sequence of multiple-valued or binary-valued input patterns. The binary-valued SAHN derived from the Adaptive Hamming Net (AHN) is functionally equivalent to a simplified ARTMAP, which is specifically designed to establish many-to-one mappings. The generalization to learning multiple-valued input patterns is achieved by incorporating multiple-valued logic into the AHN. In this paper, we examine some useful properties of learning in a P-valued SAHN. In particular, an upper bound is derived on the number of epochs required by the P-valued SAHN to learn a list of input-output pairs that is repeatedly presented to the architecture. Furthermore, we connect the P-valued SAHN with the binary-valued SAHN via the thermometer code.

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Couellan, Nicolas. "A note on supervised classification and Nash-equilibrium problems." RAIRO - Operations Research 51, no. 2 (February 27, 2017): 329–41. http://dx.doi.org/10.1051/ro/2016024.

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In this note, we investigate connections between supervised classification and (Generalized) Nash equilibrium problems (NEP & GNEP). For the specific case of support vector machines (SVM), we exploit the geometric properties of class separation in the dual space to formulate a non-cooperative game. NEP and Generalized NEP formulations are proposed for both binary and multi-class SVM problems.

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Binol, Hamidullah, Huseyin Cukur, and Abdullah Bal. "A supervised discriminant subspaces-based ensemble learning for binary classification." International Journal of Advanced Computer Research 6, no. 27 (October 3, 2016): 209–14. http://dx.doi.org/10.19101/ijacr.2016.627008.

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Kalakech, Mariam, Alice Porebski, Nicolas Vandenbroucke, and Denis Hamad. "Unsupervised Local Binary Pattern Histogram Selection Scores for Color Texture Classification." Journal of Imaging 4, no. 10 (September 28, 2018): 112. http://dx.doi.org/10.3390/jimaging4100112.

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These last few years, several supervised scores have been proposed in the literature to select histograms. Applied to color texture classification problems, these scores have improved the accuracy by selecting the most discriminant histograms among a set of available ones computed from a color image. In this paper, two new scores are proposed to select histograms: The adapted Variance score and the adapted Laplacian score. These new scores are computed without considering the class label of the images, contrary to what is done until now. Experiments, achieved on OuTex, USPTex, and BarkTex sets, show that these unsupervised scores give as good results as the supervised ones for LBP histogram selection.

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LU, Jia. "Semi-supervised binary classification algorithm based on global and local regularization." Journal of Computer Applications 32, no. 3 (April 1, 2013): 643–45. http://dx.doi.org/10.3724/sp.j.1087.2012.00643.

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Süveges, M., F. Barblan, I. Lecoeur-Taïbi, A. Prša, B. Holl, L. Eyer, A. Kochoska, N. Mowlavi, and L. Rimoldini. "Gaiaeclipsing binary and multiple systems. Supervised classification and self-organizing maps." Astronomy & Astrophysics 603 (July 2017): A117. http://dx.doi.org/10.1051/0004-6361/201629710.

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Huang, Liang, Rui Xuan Li, Kun Mei Wen, and Xi Wu Gu. "A Self Training Semi-Supervised Truncated Kernel Projection Machine for Link Prediction." Advanced Materials Research 580 (October 2012): 369–73. http://dx.doi.org/10.4028/www.scientific.net/amr.580.369.

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With the large amount of complex network data becoming available in the web, link prediction has become a popular research field of data mining. We focus on the link prediction task which can be formulated as a binary classification problem in social network. To treat this problem, a sparse semi-supervised classification algorithm called Self Training Semi-supervised Truncated Kernel Projection Machine (STKPM), based on empirical feature selection, is proposed for link prediction. Experimental results show that the proposed algorithm outperformed several outstanding learning algorithms with smaller test errors and more stability.

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Chhabra, Megha, Manoj Kumar Shukla, and Kiran Kumar Ravulakollu. "Boosting the classification performance of latent fingerprint segmentation using cascade of classifiers." Intelligent Decision Technologies 14, no. 3 (September 29, 2020): 359–71. http://dx.doi.org/10.3233/idt-190105.

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Segmentation and classification of latent fingerprints is a young challenging area of research. Latent fingerprints are unintentional fingermarks. These marks are ridge patterns left at crime scenes, lifted with latent or unclear view of fingermarks, making it difficult to find the guilty party. The segmentation of lifted images of such finger impressions comes with some unique challenges in domain such as poor quality images, incomplete ridge patterns, overlapping prints etc. The classification of poorly acquired data can be improved with image pre-processing, feeding all or optimal set of features extracted to suitable classifiers etc. Our classification system proposes two main steps. First, various effective extracted features are compartmentalised into maximal independent sets with high correlation value, Second, conventional supervised technique based binary classifiers are combined into a cascade/stack of classifiers. These classifiers are fed with all or optimal feature set(s) for binary classification of fingermarks as ridge patterns from non-ridge background. The experimentation shows improvement in accuracy rate on IIIT-D database with supervised algorithms.

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Li, Luoqing, Chuanwu Yang, and Qiwei Xie. "1D embedding multi-category classification methods." International Journal of Wavelets, Multiresolution and Information Processing 14, no. 02 (March 2016): 1640006. http://dx.doi.org/10.1142/s0219691316400063.

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In this paper, we propose a novel semi-supervised multi-category classification method based on one-dimensional (1D) multi-embedding. Based on the multiple 1D embedding based interpolation technique, we embed the high-dimensional data into several different 1D manifolds and perform binary classification firstly. Then we construct the multi-category classifiers by means of one-versus-rest and one-versus-one strategies separately. A weight strategy is employed in our algorithm for improving the classification performance. The proposed method shows promising results in the classification of handwritten digits and facial images.

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Baccianella, Stefano, Andrea Esuli, and Fabrizio Sebastiani. "Feature Selection for Ordinal Text Classification." Neural Computation 26, no. 3 (March 2014): 557–91. http://dx.doi.org/10.1162/neco_a_00558.

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Ordinal classification (also known as ordinal regression) is a supervised learning task that consists of estimating the rating of a data item on a fixed, discrete rating scale. This problem is receiving increased attention from the sentiment analysis and opinion mining community due to the importance of automatically rating large amounts of product review data in digital form. As in other supervised learning tasks such as binary or multiclass classification, feature selection is often needed in order to improve efficiency and avoid overfitting. However, although feature selection has been extensively studied for other classification tasks, it has not for ordinal classification. In this letter, we present six novel feature selection methods that we have specifically devised for ordinal classification and test them on two data sets of product review data against three methods previously known from the literature, using two learning algorithms from the support vector regression tradition. The experimental results show that all six proposed metrics largely outperform all three baseline techniques (and are more stable than these others by an order of magnitude), on both data sets and for both learning algorithms.

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Zhao, Xi, Qiyu Bai, and Shiguo Bai. "Simple nonparallel laplacian SVM for semi-supervised learning on binary classification problem." Intelligent Data Analysis 20, no. 6 (November 8, 2016): 1307–28. http://dx.doi.org/10.3233/ida-150236.

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Tavernier, Joris, Jaak Simm, Karl Meerbergen, Joerg Kurt Wegner, Hugo Ceulemans, and Yves Moreau. "Fast semi-supervised discriminant analysis for binary classification of large data sets." Pattern Recognition 91 (July 2019): 86–99. http://dx.doi.org/10.1016/j.patcog.2019.02.015.

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Yan, Xin, Yanqin Bai, Shu-Cherng Fang, and Jian Luo. "A proximal quadratic surface support vector machine for semi-supervised binary classification." Soft Computing 22, no. 20 (August 9, 2017): 6905–19. http://dx.doi.org/10.1007/s00500-017-2751-z.

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Rao, K. V. Ramana, and P. Rajesh Kumar. "Backscattering coefficient measurement and land use land cover classification using ENVI SAT ASAR data." International Journal of Engineering & Technology 7, no. 2.14 (April 12, 2018): 529. http://dx.doi.org/10.14419/ijet.v7i2.9834.

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The polarimetric SAR data of the space borne sensor, ENVISAT-ASAR (Environmental Satellite - Academic & Science Astronomy & Space Science) has been used for the land use land cover classification of the study area. It was an earth observing satellite operated by the European Space Agency (ESA). Its mission was to observe the earth and monitor critical aspects of the environment such as climatic changes on the earth at the local, regional and global levels. The data set of this sensor is a dual co-polarization amplitude data consisting of HH and VV channels. Initially various incidence angle images such as sigma naught, beta naught and gamma naught have been generated for both HH and VV polarizations. Then the backscattering coefficients of different features such as water, bare soil, vegetation and urban have been calculated. The backscattering coefficient values of the HH polarization are high compared to the values that are obtained with VV polarization. Then the land use land cover classification has been done by implementing different supervised classification algorithms. These classification methods are Parallelepiped, Minimum Distance, Mahalanobis, Maximum Likelihood, Binary Coding and Support Vector Machine. Then the accuracy measurements have been done for all these classification methods. In the present study the accuracy results obtained with the supervised Support Vector Machine classification algorithm are more compared to the accuracy results obtained with the other supervised classification methods.

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Hansen, Marie J., Nana Ø. Rasmussen, and Grace Chung. "A method of extracting the number of trial participants from abstracts describing randomized controlled trials." Journal of Telemedicine and Telecare 14, no. 7 (October 2008): 354–58. http://dx.doi.org/10.1258/jtt.2008.007007.

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We have developed a method for extracting the number of trial participants from abstracts describing randomized controlled trials (RCTs); the number of trial participants may be an indication of the reliability of the trial. The method depends on statistical natural language processing. The number of interest was determined by a binary supervised classification based on a support vector machine algorithm. The method was trialled on 223 abstracts in which the number of trial participants was identified manually to act as a gold standard. Automatic extraction resulted in 2 false-positive and 19 false-negative classifications. The algorithm was capable of extracting the number of trial participants with an accuracy of 97% and an F-measure of 0.84. The algorithm may improve the selection of relevant articles in regard to question-answering, and hence may assist in decision-making.

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Novikov, Viktor N. "Synthesis of a Fuzzy Logic Model Rule Base through Supervised Learning." Vestnik MEI 5, no. 5 (2020): 112–20. http://dx.doi.org/10.24160/1993-6982-2020-5-112-120.

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Intelligent information systems are at present actively incorporated in almost all industry fields. Out of many approaches to construction of such systems, the following two methods are worthy of noting: a method involving the use of expert knowledge, which include, in particular, fuzzy modeling), and a method of supervised machine learning, which estimate this knowledge from the available data marked depending on the objectresponse pairs. Each of these methods has its essential advantages and drawbacks. A combined use of both the approaches for solving the classification case is given. The proposed method for training a fuzzy classifier includes fuzzification of the input features of objects, shaping of logical rule conditions for the fuzzy model rule base, and selection of the most typical rule conclusions for filling the relational matrix. The terms of input features and their membership functions behave as the fuzzy model hyperparameters. As an example, the case of binary classification in a two-dimensional coordinate space with a nonlinear distribution of classes is considered. The sets of terms and membership functions ensuring high-quality classification on the training and test data have been found for each of the coordinates. The fuzzy classifier trained using the proposed method is able to solve a number of binary and multi-class classification cases. It can be applied in a situation involving difficulties in specifying an a priori rule base, and supervised learning is allowed to be used as a solution. Since the approach has logical rules at its heart, transparency of the model is ensured, and explanations to the results yielded by the model can be done.

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YANG, ZHI-XIA. "NONPARALLEL HYPERPLANES PROXIMAL CLASSIFIERS BASED ON MANIFOLD REGULARIZATION FOR LABELED AND UNLABELED EXAMPLES." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 05 (August 2013): 1350015. http://dx.doi.org/10.1142/s0218001413500158.

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In this paper, we propose two Laplacian nonparallel hyperplane proximal classifiers (LapNPPCs) for semi-supervised and full-supervised classification problem respectively by adding manifold regularization terms. Due to the manifold regularization terms, our LapNPPCs are able to exploit the intrinsic structure of the patterns of the training set. Furthermore, our classifiers only need to solve two systems of linear equations rather than two quadratic programming (QP) problems as needed in Laplacian twin support vector machine (LapTSVM) (Z. Qi, Y. Tian and Y. Shi, Neural Netw.35 (2012) 46–53). Numerical experiments on toy and UCI benchmark datasets show that the accuracy of our LapNPPCs is comparable with other classifiers, such as the standard SVM, TWSVM and LapTSVM, etc. It is also the case that based on our LapNPPCs, some other TWSVM type classifiers with manifold regularization can be constructed by choosing different norms and loss functions to deal with semi-supervised binary and multi-class classification problems.

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Tenenhaus, Arthur, Alain Giron, Emmanuel Viennet, Michel Béra, Gilbert Saporta, and Bernard Fertil. "Kernel logistic PLS: A tool for supervised nonlinear dimensionality reduction and binary classification." Computational Statistics & Data Analysis 51, no. 9 (May 2007): 4083–100. http://dx.doi.org/10.1016/j.csda.2007.01.004.

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Issac, K., K. Bharadwaj, N. Bharanidharan, and Harikumar Rajaguru. "Investigation on enhancing the binary classification accuracy of supervised classifiers using various transforms." IOP Conference Series: Materials Science and Engineering 1084, no. 1 (March 1, 2021): 012032. http://dx.doi.org/10.1088/1757-899x/1084/1/012032.

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Wiebe, Nathan, Ashish Kapoor, and Krysta M. Svore. "Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning." Quantum Information and Computation 15, no. 3&4 (March 2015): 316–56. http://dx.doi.org/10.26421/qic15.3-4-7.

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We present quantum algorithms for performing nearest-neighbor learning and $k$--means clustering. At the core of our algorithms are fast and coherent quantum methods for computing the Euclidean distance both directly and via the inner product which we couple with methods for performing amplitude estimation that do not require measurement. We prove upper bounds on the number of queries to the input data required to compute such distances and find the nearest vector to a given test example. In the worst case, our quantum algorithms lead to polynomial reductions in query complexity relative to Monte Carlo algorithms. We also study the performance of our quantum nearest-neighbor algorithms on several real-world binary classification tasks and find that the classification accuracy is competitive with classical methods.

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Inthiyaz, Syed, B. T.P.Madhav, and Ch Raghava Prasad. "Flower image classification with basket of features and multi layered artificial neural networks." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 642. http://dx.doi.org/10.14419/ijet.v7i1.1.10795.

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Artificial intelligence is penetrating most of the classification and recognition tasks performed by a computer. This work proposes to classify flower images based on features extracted during segmentation and after segmentation using multiple layered neural networks. The segmentation models used are watershed, wavelet, wavelet fusion model, supervised active contours based on shape, color and Local binary pattern textures and color, fused textures based active contours. Multi-dimension feature vectors are constructed from these segmented results for each indexed flower image labelled with their name. Each feature becomes input to a neuron in various feature layers and error back propagation algorithm with convex optimization structure trains these multiple feature layers. Testing with different flower images sets from multiple sources resulted in average classification accuracy of 92% for shape, color and texture supervised active contour segmented flower images.

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Tagny-Ngompé, Gildas, Stéphane Mussard, Guillaume Zambrano, Sébastien Harispe, and Jacky Montmain. "Identification of Judicial Outcomes in Judgments: A Generalized Gini-PLS Approach." Stats 3, no. 4 (September 27, 2020): 427–43. http://dx.doi.org/10.3390/stats3040027.

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This paper presents and compares several text classification models that can be used to extract the outcome of a judgment from justice decisions, i.e., legal documents summarizing the different rulings made by a judge. Such models can be used to gather important statistics about cases, e.g., success rate based on specific characteristics of cases’ parties or jurisdiction, and are therefore important for the development of Judicial prediction not to mention the study of Law enforcement in general. We propose in particular the generalized Gini-PLS which better considers the information in the distribution tails while attenuating, as in the simple Gini-PLS, the influence exerted by outliers. Modeling the studied task as a supervised binary classification, we also introduce the LOGIT-Gini-PLS suited to the explanation of a binary target variable. In addition, various technical aspects regarding the evaluated text classification approaches which consists of combinations of representations of judgments and classification algorithms are studied using an annotated corpora of French justice decisions.

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Vavilova, I. B., D. V. Dobrycheva, M. Yu Vasylenko, A. A. Elyiv, O. V. Melnyk, and V. Khramtsov. "Machine learning technique for morphological classification of galaxies from the SDSS." Astronomy & Astrophysics 648 (April 2021): A122. http://dx.doi.org/10.1051/0004-6361/202038981.

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Context. Machine learning methods are effective tools in astronomical tasks for classifying objects by their individual features. One of the promising utilities is related to the morphological classification of galaxies at different redshifts. Aims. We use the photometry-based approach for the SDSS data (1) to exploit five supervised machine learning techniques and define the most effective among them for the automated galaxy morphological classification; (2) to test the influence of photometry data on morphology classification; (3) to discuss problem points of supervised machine learning and labeling bias; and (4) to apply the best fitting machine learning methods for revealing the unknown morphological types of galaxies from the SDSS DR9 at z < 0.1. Methods. We used different galaxy classification techniques: human labeling, multi-photometry diagrams, naive Bayes, logistic regression, support-vector machine, random forest, k-nearest neighbors. Results. We present the results of a binary automated morphological classification of galaxies conducted by human labeling, multi-photometry, and five supervised machine learning methods. We applied it to the sample of galaxies from the SDSS DR9 with redshifts of 0.02 < z < 0.1 and absolute stellar magnitudes of −24m < Mr < −19.4m. For the analysis we used absolute magnitudes Mu, Mg, Mr, Mi, Mz; color indices Mu − Mr, Mg − Mi, Mu − Mg, Mr − Mz; and the inverse concentration index to the center R50/R90. We determined the ability of each method to predict the morphological type, and verified various dependencies of the method’s accuracy on redshifts, human labeling, morphological shape, and overlap of different morphological types for galaxies with the same color indices. We find that the morphology based on the supervised machine learning methods trained over photometric parameters demonstrates significantly less bias than the morphology based on citizen-science classifiers. Conclusions. The support-vector machine and random forest methods with Scikit-learn software machine learning library in Python provide the highest accuracy for the binary galaxy morphological classification. Specifically, the success rate is 96.4% for support-vector machine (96.1% early E and 96.9% late L types) and 95.5% for random forest (96.7% early E and 92.8% late L types). Applying the support-vector machine for the sample of 316 031 galaxies from the SDSS DR9 at z < 0.1 with unknown morphological types, we found 139 659 E and 176 372 L types among them.

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Huang, He, Mierk Schwabe, and Cheng-Ran Du. "Identification of the Interface in a Binary Complex Plasma Using Machine Learning." Journal of Imaging 5, no. 3 (March 12, 2019): 36. http://dx.doi.org/10.3390/jimaging5030036.

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A binary complex plasma consists of two different types of dust particles in an ionized gas. Due to the spinodal decomposition and force imbalance, particles of different masses and diameters are typically phase separated, resulting in an interface. Both external excitation and internal instability may cause the interface to move with time. Support vector machine (SVM) is a supervised machine learning method that can be very effective for multi-class classification. We applied an SVM classification method based on image brightness to locate the interface in a binary complex plasma. Taking the scaled mean and variance as features, three areas, namely small particles, big particles and plasma without dust particles, were distinguished, leading to the identification of the interface between small and big particles.

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Ben Nasr, Mohamed Chiheb, Sofia Ben Jebara, Samuel Otis, Bessam Abdulrazak, and Neila Mezghani. "A Spectral-Based Approach for BCG Signal Content Classification." Sensors 21, no. 3 (February 2, 2021): 1020. http://dx.doi.org/10.3390/s21031020.

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This paper has two objectives: the first is to generate two binary flags to indicate useful frames permitting the measurement of cardiac and respiratory rates from Ballistocardiogram (BCG) signals—in fact, human body activities during measurements can disturb the BCG signal content, leading to difficulties in vital sign measurement; the second objective is to achieve refined BCG signal segmentation according to these activities. The proposed framework makes use of two approaches: an unsupervised classification based on the Gaussian Mixture Model (GMM) and a supervised classification based on K-Nearest Neighbors (KNN). Both of these approaches consider two spectral features, namely the Spectral Flatness Measure (SFM) and Spectral Centroid (SC), determined during the feature extraction step. Unsupervised classification is used to explore the content of the BCG signals, justifying the existence of different classes and permitting the definition of useful hyper-parameters for effective segmentation. In contrast, the considered supervised classification approach aims to determine if the BCG signal content allows the measurement of the heart rate (HR) and the respiratory rate (RR) or not. Furthermore, two levels of supervised classification are used to classify human-body activities into many realistic classes from the BCG signal (e.g., coughing, holding breath, air expiration, movement, et al.). The first one considers frame-by-frame classification, while the second one, aiming to boost the segmentation performance, transforms the frame-by-frame SFM and SC features into temporal series which track the temporal variation of the measures of the BCG signal. The proposed approach constitutes a novelty in this field and represents a powerful method to segment BCG signals according to human body activities, resulting in an accuracy of 94.6%.

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Kumar, Akshi, Arunima Jaiswal, Shikhar Garg, Shobhit Verma, and Siddhant Kumar. "Sentiment Analysis Using Cuckoo Search for Optimized Feature Selection on Kaggle Tweets." International Journal of Information Retrieval Research 9, no. 1 (January 2019): 1–15. http://dx.doi.org/10.4018/ijirr.2019010101.

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Selecting the optimal set of features to determine sentiment in online textual content is imperative for superior classification results. Optimal feature selection is computationally hard task and fosters the need for devising novel techniques to improve the classifier performance. In this work, the binary adaptation of cuckoo search (nature inspired, meta-heuristic algorithm) known as the Binary Cuckoo Search is proposed for the optimum feature selection for a sentiment analysis of textual online content. The baseline supervised learning techniques such as SVM, etc., have been firstly implemented with the traditional tf-idf model and then with the novel feature optimization model. Benchmark Kaggle dataset, which includes a collection of tweets is considered to report the results. The results are assessed on the basis of performance accuracy. Empirical analysis validates that the proposed implementation of a binary cuckoo search for feature selection optimization in a sentiment analysis task outperforms the elementary supervised algorithms based on the conventional tf-idf score.

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Kim, Sung Wook, Young Gon Lee, Bayu Adhi Tama, and Seungchul Lee. "Reliability-Enhanced Camera Lens Module Classification Using Semi-Supervised Regression Method." Applied Sciences 10, no. 11 (May 31, 2020): 3832. http://dx.doi.org/10.3390/app10113832.

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Artificial intelligence has become the primary issue in the era of Industry 4.0, accelerating the realization of a self-driven smart factory. It is transforming various manufacturing sectors including the assembly line for a camera lens module. The recent development of bezel-less smartphones necessitates a large-scale production of the camera lens module. However, assembling the necessary parts of a module needs much room to be improved since the procedure followed by its inspection is costly and time-consuming. Consequently, the collection of labeled data is often limited. In this study, a reliable means to predict the state of an unseen camera lens module using simple semi-supervised regression is proposed. Here, an experimental study to investigate the effect of different numbers of training samples is demonstrated. The increased amount of data using simple pseudo-labeling means is shown to improve the general performance of deep neural network for the prediction of Modulation Transfer Function (MTF) by as much as 18%, 15% and 25% in terms of RMSE, MAE and R squared. The cross-validation technique is used to ensure a generalized predictive performance. Furthermore, binary classification is conducted based on a threshold value for MTF to finally demonstrate the better prediction outcome in a real-world scenario. As a result, the overall accuracy, recall, specificity and f1-score are increased by 11.3%, 9%, 1.6% and 7.6% showing that the classification of camera lens module has been improved through the suggested semi-supervised regression method.

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Hatcholli Seere, Santhosh Kumar, and K. Karibasappa. "Threshold Segmentation and Watershed Segmentation Algorithm for Brain Tumor Detection using Support Vector Machine." European Journal of Engineering Research and Science 5, no. 4 (April 26, 2020): 516–19. http://dx.doi.org/10.24018/ejers.2020.5.4.1902.

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Brain Tumor is a dangerous disease. The chance of the death is more in case of the brain tumor. The method of detection and classification of brain tumor is by human intervention with use of medical resonant brain images. MR Images may contain noise or blur caused by MRI operator performance which can lead to difficult in classification. We can apply effective segmentation techniques to partition the image and apply the classification technique. Support Vector machine is the best classification tool we identified as part of this work. The use Support Vector Machine show great potential in this field. SVM is a binary Classifier based on supervised learning which gives better result than other classifiers. SVM classifies between two classes by constructing hyper plane in high-dimensional feature space which can be used for classification.

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Abasht, Behnam, Michael B. Papah, and Jing Qiu. "Evidence of vascular endothelial dysfunction in Wooden Breast disorder in chickens: Insights through gene expression analysis, ultra-structural evaluation and supervised machine learning methods." PLOS ONE 16, no. 1 (January 4, 2021): e0243983. http://dx.doi.org/10.1371/journal.pone.0243983.

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Several gene expression studies have been previously conducted to characterize molecular basis of Wooden Breast myopathy in commercial broiler chickens. These studies have generally used a limited sample size and relied on a binary disease outcome (unaffected or affected by Wooden Breast), which are appropriate for an initial investigation. However, to identify biomarkers of disease severity and development, it is necessary to use a large number of samples with a varying degree of disease severity. Therefore, in this study, we assayed a relatively large number of samples (n = 96) harvested from the pectoralis major muscle of unaffected (U), partially affected (P) and markedly affected (A) chickens. Gene expression analysis was conducted using the nCounter MAX Analysis System and data were analyzed using four different supervised machine-learning methods, including support vector machines (SVM), random forests (RF), elastic net logistic regression (ENET) and Lasso logistic regression (LASSO). The SVM method achieved the highest prediction accuracy for both three-class (U, P and A) and two-class (U and P+A) classifications with 94% prediction accuracy for two-class classification and 85% for three-class classification. The results also identified biomarkers of Wooden Breast severity and development. Additionally, gene expression analysis and ultrastructural evaluations provided evidence of vascular endothelial cell dysfunction in the early pathogenesis of Wooden Breast.

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Urbanczik, Robert, and Walter Senn. "A Gradient Learning Rule for the Tempotron." Neural Computation 21, no. 2 (February 2009): 340–52. http://dx.doi.org/10.1162/neco.2008.09-07-605.

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We introduce a new supervised learning rule for the tempotron task: the binary classification of input spike trains by an integrate-and-fire neuron that encodes its decision by firing or not firing. The rule is based on the gradient of a cost function, is found to have enhanced performance, and does not rely on a specific reset mechanism in the integrate-and-fire neuron.

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Astorino, Annabella, Antonio Fuduli, Giovanni Giallombardo, and Giovanna Miglionico. "SVM-Based Multiple Instance Classification via DC Optimization." Algorithms 12, no. 12 (November 23, 2019): 249. http://dx.doi.org/10.3390/a12120249.

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A multiple instance learning problem consists of categorizing objects, each represented as a set (bag) of points. Unlike the supervised classification paradigm, where each point of the training set is labeled, the labels are only associated with bags, while the labels of the points inside the bags are unknown. We focus on the binary classification case, where the objective is to discriminate between positive and negative bags using a separating surface. Adopting a support vector machine setting at the training level, the problem of minimizing the classification-error function can be formulated as a nonconvex nonsmooth unconstrained program. We propose a difference-of-convex (DC) decomposition of the nonconvex function, which we face using an appropriate nonsmooth DC algorithm. Some of the numerical results on benchmark data sets are reported.

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Logan, C. H. A., and S. Fotopoulou. "Unsupervised star, galaxy, QSO classification." Astronomy & Astrophysics 633 (January 2020): A154. http://dx.doi.org/10.1051/0004-6361/201936648.

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Context. Classification will be an important first step for upcoming surveys aimed at detecting billions of new sources, such as LSST and Euclid, as well as DESI, 4MOST, and MOONS. The application of traditional methods of model fitting and colour-colour selections will face significant computational constraints, while machine-learning methods offer a viable approach to tackle datasets of that volume. Aims. While supervised learning methods can prove very useful for classification tasks, the creation of representative and accurate training sets is a task that consumes a great deal of resources and time. We present a viable alternative using an unsupervised machine learning method to separate stars, galaxies and QSOs using photometric data. Methods. The heart of our work uses Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to find the star, galaxy, and QSO clusters in a multidimensional colour space. We optimized the hyperparameters and input attributes of three separate HDBSCAN runs, each to select a particular object class and, thus, treat the output of each separate run as a binary classifier. We subsequently consolidated the output to give our final classifications, optimized on the basis of their F1 scores. We explored the use of Random Forest and PCA as part of the pre-processing stage for feature selection and dimensionality reduction. Results. Using our dataset of ∼50 000 spectroscopically labelled objects we obtain F1 scores of 98.9, 98.9, and 93.13 respectively for star, galaxy, and QSO selection using our unsupervised learning method. We find that careful attribute selection is a vital part of accurate classification with HDBSCAN. We applied our classification to a subset of the SDSS spectroscopic catalogue and demonstrated the potential of our approach in correcting misclassified spectra useful for DESI and 4MOST. Finally, we created a multiwavelength catalogue of 2.7 million sources using the KiDS, VIKING, and ALLWISE surveys and published corresponding classifications and photometric redshifts.

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BÁLYA, DÁVID. "CNN UNIVERSAL MACHINE AS CLASSIFICATON PLATFORM: AN ART-LIKE CLUSTERING ALGORITHM." International Journal of Neural Systems 13, no. 06 (December 2003): 415–25. http://dx.doi.org/10.1142/s0129065703001807.

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Fast and robust classification of feature vectors is a crucial task in a number of real-time systems. A cellular neural/nonlinear network universal machine (CNN-UM) can be very efficient as a feature detector. The next step is to post-process the results for object recognition. This paper shows how a robust classification scheme based on adaptive resonance theory (ART) can be mapped to the CNN-UM. Moreover, this mapping is general enough to include different types of feed-forward neural networks. The designed analogic CNN algorithm is capable of classifying the extracted feature vectors keeping the advantages of the ART networks, such as robust, plastic and fault-tolerant behaviors. An analogic algorithm is presented for unsupervised classification with tunable sensitivity and automatic new class creation. The algorithm is extended for supervised classification. The presented binary feature vector classification is implemented on the existing standard CNN-UM chips for fast classification. The experimental evaluation shows promising performance after 100% accuracy on the training set.

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S. Tidake, Vaishali, and Shirish S. Sane. "Multi-label Classification: a survey." International Journal of Engineering & Technology 7, no. 4.19 (November 27, 2018): 1045. http://dx.doi.org/10.14419/ijet.v7i4.19.28284.

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Wide use of internet generates huge data which needs proper organization leading to text categorization. Earlier it was found that a document describes one category. Soon it was realized that it can describe multiple categories simultaneously. This scenario reveals the use of multi-label classification, a supervised learning approach, which assigns a predefined set of labels to an object by looking at its characteristics. Earlier used in text categorization, but soon it became the choice of researchers for wide applications like marketing, multimedia annotation, bioinformatics. Two most common approaches for multi-label classification are transformation which takes the benefit of existing single label classifiers preceded by converting multi-label data to single label, or an adaptation which designs classifiers which handle multi-label data directly. Another popular approach is ensemble of multiple classifiers taking votes of all. Other approaches are also available namely algorithm independent and algorithm dependent approach. Based on results produced, suitable metric is used for example or label wise evaluation which depends on whether prediction is binary or ranking. Every approach offers benefits and issues like loss of label dependency in transformation, complexity in case of adaptation, improvement in results using ensemble which should be considered during design of underlying application.

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Karlos, Stamatis, Georgios Kostopoulos, and Sotiris Kotsiantis. "A Soft-Voting Ensemble Based Co-Training Scheme Using Static Selection for Binary Classification Problems." Algorithms 13, no. 1 (January 16, 2020): 26. http://dx.doi.org/10.3390/a13010026.

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In recent years, a forward-looking subfield of machine learning has emerged with important applications in a variety of scientific fields. Semi-supervised learning is increasingly being recognized as a burgeoning area embracing a plethora of efficient methods and algorithms seeking to exploit a small pool of labeled examples together with a large pool of unlabeled ones in the most efficient way. Co-training is a representative semi-supervised classification algorithm originally based on the assumption that each example can be described by two distinct feature sets, usually referred to as views. Since such an assumption can hardly be met in real world problems, several variants of the co-training algorithm have been proposed dealing with the absence or existence of a naturally two-view feature split. In this context, a Static Selection Ensemble-based co-training scheme operating under a random feature split strategy is outlined regarding binary classification problems, where the type of the base ensemble learner is a soft-Voting one composed of two participants. Ensemble methods are commonly used to boost the predictive performance of learning models by using a set of different classifiers, while the Static Ensemble Selection approach seeks to find the most suitable structure of ensemble classifier based on a specific criterion through a pool of candidate classifiers. The efficacy of the proposed scheme is verified through several experiments on a plethora of benchmark datasets as statistically confirmed by the Friedman Aligned Ranks non-parametric test over the behavior of classification accuracy, F1-score, and Area Under Curve metrics.

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Kilicoglu, Halil, Graciela Rosemblat, Mario Malički, and Gerben ter Riet. "Automatic recognition of self-acknowledged limitations in clinical research literature." Journal of the American Medical Informatics Association 25, no. 7 (April 28, 2018): 855–61. http://dx.doi.org/10.1093/jamia/ocy038.

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Abstract Objective To automatically recognize self-acknowledged limitations in clinical research publications to support efforts in improving research transparency. Methods To develop our recognition methods, we used a set of 8431 sentences from 1197 PubMed Central articles. A subset of these sentences was manually annotated for training/testing, and inter-annotator agreement was calculated. We cast the recognition problem as a binary classification task, in which we determine whether a given sentence from a publication discusses self-acknowledged limitations or not. We experimented with three methods: a rule-based approach based on document structure, supervised machine learning, and a semi-supervised method that uses self-training to expand the training set in order to improve classification performance. The machine learning algorithms used were logistic regression (LR) and support vector machines (SVM). Results Annotators had good agreement in labeling limitation sentences (Krippendorff’s α = 0.781). Of the three methods used, the rule-based method yielded the best performance with 91.5% accuracy (95% CI [90.1-92.9]), while self-training with SVM led to a small improvement over fully supervised learning (89.9%, 95% CI [88.4-91.4] vs 89.6%, 95% CI [88.1-91.1]). Conclusions The approach presented can be incorporated into the workflows of stakeholders focusing on research transparency to improve reporting of limitations in clinical studies.

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Vogrincic, Sergeja, and Zoran Bosnic. "Ontology-based multi-label classification of economic articles." Computer Science and Information Systems 8, no. 1 (2011): 101–19. http://dx.doi.org/10.2298/csis100420034v.

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The paper presents an approach to the task of automatic document categorization in the field of economics. Since the documents can be annotated with multiple keywords (labels), we approach this task by applying and evaluating multi-label classification methods of supervised machine learning. We describe forming a test corpus of 1015 economic documents that we automatically classify using a tool which integrates ontology construction with text mining methods. In our experimental work, we evaluate three groups of multi-label classification approaches: transformation to single-class problems, specialized multi-label models, and hierarchical/ranking models. The classification accuracies of all tested classification models indicate that there is a potential for using all of the evaluated methods to solve this task. The results show the benefits of using complex groups of approaches which benefit from exploiting dependence between the labels. A good alternative to these approaches is also single-class naive Bayes classifiers coupled with the binary relevance transformation approach.

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Jiang, Wenxin. "On the Consistency of Bayesian Variable Selection for High Dimensional Binary Regression and Classification." Neural Computation 18, no. 11 (November 2006): 2762–76. http://dx.doi.org/10.1162/neco.2006.18.11.2762.

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Modern data mining and bioinformatics have presented an important playground for statistical learning techniques, where the number of input variables is possibly much larger than the sample size of the training data. In supervised learning, logistic regression or probit regression can be used to model a binary output and form perceptron classification rules based on Bayesian inference. We use a prior to select a limited number of candidate variables to enter the model, applying a popular method with selection indicators. We show that this approach can induce posterior estimates of the regression functions that are consistently estimating the truth, if the true regression model is sparse in the sense that the aggregated size of the regression coefficients are bounded. The estimated regression functions therefore can also produce consistent classifiers that are asymptotically optimal for predicting future binary outputs. These provide theoretical justifications for some recent empirical successes in microarray data analysis.

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Peréz-Ortiz, M., P. Tiňo, R. Mantiuk, and C. Hervás-Martínez. "Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4715–22. http://dx.doi.org/10.1609/aaai.v33i01.33014715.

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Data augmentation is rapidly gaining attention in machine learning. Synthetic data can be generated by simple transformations or through the data distribution. In the latter case, the main challenge is to estimate the label associated to new synthetic patterns. This paper studies the effect of generating synthetic data by convex combination of patterns and the use of these as unsupervised information in a semi-supervised learning framework with support vector machines, avoiding thus the need to label synthetic examples. We perform experiments on a total of 53 binary classification datasets. Our results show that this type of data over-sampling supports the well-known cluster assumption in semi-supervised learning, showing outstanding results for small high-dimensional datasets and imbalanced learning problems.

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Iwata, Tomoharu, Akinori Fujino, and Naonori Ueda. "Semi-Supervised Learning for Maximizing the Partial AUC." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4239–46. http://dx.doi.org/10.1609/aaai.v34i04.5846.

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The partial area under a receiver operating characteristic curve (pAUC) is a performance measurement for binary classification problems that summarizes the true positive rate with the specific range of the false positive rate. Obtaining classifiers that achieve high pAUC is important in a wide variety of applications, such as cancer screening and spam filtering. Although many methods have been proposed for maximizing the pAUC, existing methods require many labeled data for training. In this paper, we propose a semi-supervised learning method for maximizing the pAUC, which trains a classifier with a small amount of labeled data and a large amount of unlabeled data. To exploit the unlabeled data, we derive two approximations of the pAUC: the first is calculated from positive and unlabeled data, and the second is calculated from negative and unlabeled data. A classifier is trained by maximizing the weighted sum of the two approximations of the pAUC and the pAUC that is calculated from positive and negative data. With experiments using various datasets, we demonstrate that the proposed method achieves higher test pAUCs than existing methods.

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Santoyo-Ramón, José Antonio, Eduardo Casilari, and José Manuel Cano-García. "A Study of One-Class Classification Algorithms for Wearable Fall Sensors." Biosensors 11, no. 8 (August 19, 2021): 284. http://dx.doi.org/10.3390/bios11080284.

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In recent years, the popularity of wearable devices has fostered the investigation of automatic fall detection systems based on the analysis of the signals captured by transportable inertial sensors. Due to the complexity and variety of human movements, the detection algorithms that offer the best performance when discriminating falls from conventional Activities of Daily Living (ADLs) are those built on machine learning and deep learning mechanisms. In this regard, supervised machine learning binary classification methods have been massively employed by the related literature. However, the learning phase of these algorithms requires mobility patterns caused by falls, which are very difficult to obtain in realistic application scenarios. An interesting alternative is offered by One-Class Classifiers (OCCs), which can be exclusively trained and configured with movement traces of a single type (ADLs). In this paper, a systematic study of the performance of various typical OCCs (for diverse sets of input features and hyperparameters) is performed when applied to nine public repositories of falls and ADLs. The results show the potentials of these classifiers, which are capable of achieving performance metrics very similar to those of supervised algorithms (with values for the specificity and the sensitivity higher than 95%). However, the study warns of the need to have a wide variety of types of ADLs when training OCCs, since activities with a high degree of mobility can significantly increase the frequency of false alarms (ADLs identified as falls) if not considered in the data subsets used for training.

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Demirsoz, Orhan, and Rifat Ozcan. "Classification of news-related tweets." Journal of Information Science 43, no. 4 (June 1, 2016): 509–24. http://dx.doi.org/10.1177/0165551516653082.

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It is important to obtain public opinion about a news article. Microblogs such as Twitter are popular and an important medium for people to share ideas. An important portion of tweets are related to news or events. Our aim is to find tweets about newspaper reports and measure the popularity of these reports on Twitter. However, it is a challenging task to match informal and very short tweets with formal news reports. In this study, we formulate this problem as a supervised classification task. We propose to form a training set using tweets containing a link to the news and the content of the same news article. We preprocess tweets by removing unnecessary words and symbols and apply stemming by means of morphological analysers. We apply binary classifiers and anomaly detection to this task. We also propose a textual similarity-based approach. We observed that preprocessing of tweets increases accuracy. The textual similarity method obtains results with the highest recognition rate. Success increases in some cases when report text is used with tweets containing a link to the news report within the training set of classification studies. We propose that this study, which is made directly in consideration of tweet texts that measure the trends of national newspaper reports on social media, has a higher significance when compared to Twitter analyses made by using a hashtag. Given the limited number of scientific studies on Turkish tweets, this study makes a contribution to the literature.

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Gao, Feng, Qun Wang, Junyu Dong, and Qizhi Xu. "Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs." Remote Sensing 10, no. 8 (August 12, 2018): 1271. http://dx.doi.org/10.3390/rs10081271.

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Hyperspectral image classification has been acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundance of spectral and spatial information has provided great opportunities to effectively characterize and identify ground materials. In this paper, we propose a spectral and spatial classification framework for hyperspectral images based on Random Multi-Graphs (RMGs). The RMG is a graph-based ensemble learning method, which is rarely considered in hyperspectral image classification. It is empirically verified that the semi-supervised RMG deals well with small sample setting problems. This kind of problem is very common in hyperspectral image applications. In the proposed method, spatial features are extracted based on linear prediction error analysis and local binary patterns; spatial features and spectral features are then stacked into high dimensional vectors. The high dimensional vectors are fed into the RMG for classification. By randomly selecting a subset of features to create a graph, the proposed method can achieve excellent classification performance. The experiments on three real hyperspectral datasets have demonstrated that the proposed method exhibits better performance than several closely related methods.

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Choi, Jongkwon, Youngmin Choo, and Keunhwa Lee. "Acoustic Classification of Surface and Underwater Vessels in the Ocean Using Supervised Machine Learning." Sensors 19, no. 16 (August 9, 2019): 3492. http://dx.doi.org/10.3390/s19163492.

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Four data-driven methods—random forest (RF), support vector machine (SVM), feed-forward neural network (FNN), and convolutional neural network (CNN)—are applied to discriminate surface and underwater vessels in the ocean using low-frequency acoustic pressure data. Acoustic data are modeled considering a vertical line array by a Monte Carlo simulation using the underwater acoustic propagation model, KRAKEN, in the ocean environment of East Sea in Korea. The raw data are preprocessed and reorganized into the phone-space cross-spectral density matrix (pCSDM) and mode-space cross-spectral density matrix (mCSDM). Two additional matrices are generated using the absolute values of matrix elements in each CSDM. Each of these four matrices is used as input data for supervised machine learning. Binary classification is performed by using RF, SVM, FNN, and CNN, and the obtained results are compared. All machine-learning algorithms show an accuracy of >95% for three types of input data—the pCSDM, mCSDM, and mCSDM with the absolute matrix elements. The CNN is the best in terms of low percent error. In particular, the result using the complex pCSDM is encouraging because these data-driven methods inherently do not require environmental information. This work demonstrates the potential of machine learning to discriminate between surface and underwater vessels in the ocean.

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Zhao, Xu. "A Kernel Based Neighborhood Discriminant Submanifold Learning for Pattern Classification." Journal of Applied Mathematics 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/950349.

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We propose a novel method, called Kernel Neighborhood Discriminant Analysis (KNDA), which can be regarded as a supervised kernel extension of Locality Preserving Projection (LPP). KNDA nonlinearly maps the original data into a kernel space in which two graphs are constructed to depict the within-class submanifold and the between-class submanifold. Then a criterion function which minimizes the quotient between the within-class representation and the between-class representation of the submanifolds is designed to separate each submanifold constructed by each class. The real contribution of this paper is that we bring and extend the submanifold based algorithm to a general model and by some derivation a simple result is given by which we can classify a given object to a predefined class effectively. Experiments on the MNIST Handwritten Digits database, the Binary Alphadigits database, the ORL face database, the Extended Yale Face Database B, and a downloaded documents dataset demonstrate the effectiveness and robustness of the proposed method.

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Dadon, Alon, Moshe Mandelmilch, Eyal Ben-Dor, and Efrat Sheffer. "Sequential PCA-based Classification of Mediterranean Forest Plants using Airborne Hyperspectral Remote Sensing." Remote Sensing 11, no. 23 (November 27, 2019): 2800. http://dx.doi.org/10.3390/rs11232800.

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In recent years, hyperspectral remote sensing (HRS) has become common practice for remote analyses of the physiognomy and composition of forests. Supervised classification is often used for this purpose, but demands intensive sampling and analyses, whereas unsupervised classification often requires information retrieval out of the large HRS datasets, thereby not realizing the full potential of the technology. An improved principal component analysis-based classification (PCABC) scheme is presented and intended to provide accurate and sequential image-based unsupervised classification of Mediterranean forest species. In this study, unsupervised classification and reduction of data size are performed simultaneously by applying binary sequential thresholding to principal components, each time on a spatially reduced subscene that includes the entire spectral range. The methodology was tested on HRS data acquired by the airborne AisaFENIX HRS sensor over a Mediterranean forest in Mount Horshan, Israel. A comprehensive field-validation survey was performed, sampling 257 randomly selected individual plants. The PCABC provided highly improved results compared to the traditional unsupervised classification methodologies, reaching an overall accuracy of 91%. The presented approach may contribute to improved monitoring, management, and conservation of Mediterranean and similar forests.

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Sheng, Kekai, Weiming Dong, Menglei Chai, Guohui Wang, Peng Zhou, Feiyue Huang, Bao-Gang Hu, Rongrong Ji, and Chongyang Ma. "Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5709–16. http://dx.doi.org/10.1609/aaai.v34i04.6026.

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Visual aesthetic assessment has been an active research field for decades. Although latest methods have achieved promising performance on benchmark datasets, they typically rely on a large number of manual annotations including both aesthetic labels and related image attributes. In this paper, we revisit the problem of image aesthetic assessment from the self-supervised feature learning perspective. Our motivation is that a suitable feature representation for image aesthetic assessment should be able to distinguish different expert-designed image manipulations, which have close relationships with negative aesthetic effects. To this end, we design two novel pretext tasks to identify the types and parameters of editing operations applied to synthetic instances. The features from our pretext tasks are then adapted for a one-layer linear classifier to evaluate the performance in terms of binary aesthetic classification. We conduct extensive quantitative experiments on three benchmark datasets and demonstrate that our approach can faithfully extract aesthetics-aware features and outperform alternative pretext schemes. Moreover, we achieve comparable results to state-of-the-art supervised methods that use 10 million labels from ImageNet.

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Journal articles on the topic 'Classification binaire supervisée'

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