Готові списки джерел за темами / Label selection

Добірка наукової літератури з теми "Label selection"

Автор: Grafiati

Опубліковано: 28 вересня 2022

Оновлено: 28 січня 2023

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Статті в журналах з теми "Label selection":

Wu, Xingyu, Bingbing Jiang, Kui Yu, Huanhuan Chen, and Chunyan Miao. "Multi-Label Causal Feature Selection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6430–37. http://dx.doi.org/10.1609/aaai.v34i04.6114.

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Multi-label feature selection has received considerable attentions during the past decade. However, existing algorithms do not attempt to uncover the underlying causal mechanism, and individually solve different types of variable relationships, ignoring the mutual effects between them. Furthermore, these algorithms lack of interpretability, which can only select features for all labels, but cannot explain the correlation between a selected feature and a certain label. To address these problems, in this paper, we theoretically study the causal relationships in multi-label data, and propose a novel Markov blanket based multi-label causal feature selection (MB-MCF) algorithm. MB-MCF mines the causal mechanism of labels and features first, to obtain a complete representation of information about labels. Based on the causal relationships, MB-MCF then selects predictive features and simultaneously distinguishes common features shared by multiple labels and label-specific features owned by single labels. Experiments on real-world data sets validate that MB-MCF could automatically determine the number of selected features and simultaneously achieve the best performance compared with state-of-the-art methods. An experiment in Emotions data set further demonstrates the interpretability of MB-MCF.

Zhang, Ping, Wanfu Gao, Juncheng Hu, and Yonghao Li. "Multi-Label Feature Selection Based on High-Order Label Correlation Assumption." Entropy 22, no. 7 (July 21, 2020): 797. http://dx.doi.org/10.3390/e22070797.

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Multi-label data often involve features with high dimensionality and complicated label correlations, resulting in a great challenge for multi-label learning. Feature selection plays an important role in multi-label learning to address multi-label data. Exploring label correlations is crucial for multi-label feature selection. Previous information-theoretical-based methods employ the strategy of cumulative summation approximation to evaluate candidate features, which merely considers low-order label correlations. In fact, there exist high-order label correlations in label set, labels naturally cluster into several groups, similar labels intend to cluster into the same group, different labels belong to different groups. However, the strategy of cumulative summation approximation tends to select the features related to the groups containing more labels while ignoring the classification information of groups containing less labels. Therefore, many features related to similar labels are selected, which leads to poor classification performance. To this end, Max-Correlation term considering high-order label correlations is proposed. Additionally, we combine the Max-Correlation term with feature redundancy term to ensure that selected features are relevant to different label groups. Finally, a new method named Multi-label Feature Selection considering Max-Correlation (MCMFS) is proposed. Experimental results demonstrate the classification superiority of MCMFS in comparison to eight state-of-the-art multi-label feature selection methods.

Robitaille, Nicolas, and Simon Duchesne. "Label Fusion Strategy Selection." International Journal of Biomedical Imaging 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/431095.

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Label fusion is used in medical image segmentation to combine several different labels of the same entity into a single discrete label, potentially more accurate, with respect to the exact, sought segmentation, than the best input element. Using simulated data, we compared three existing label fusion techniques—STAPLE, Voting, and Shape-Based Averaging (SBA)—and observed that none could be considered superior depending on the dissimilarity between the input elements. We thus developed an empirical, hybrid technique called SVS, which selects the most appropriate technique to apply based on this dissimilarity. We evaluated the label fusion strategies on two- and three-dimensional simulated data and showed that SVS is superior to any of the three existing methods examined. On real data, we used SVS to perform fusions of 10 segmentations of the hippocampus and amygdala in 78 subjects from the ICBM dataset. SVS selected SBA in almost all cases, which was the most appropriate method overall.

Wang, Xiujuan, and Yuchen Zhou. "Multi-Label Feature Selection with Conditional Mutual Information." Computational Intelligence and Neuroscience 2022 (October 8, 2022): 1–13. http://dx.doi.org/10.1155/2022/9243893.

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Feature selection is an important way to optimize the efficiency and accuracy of classifiers. However, traditional feature selection methods cannot work with many kinds of data in the real world, such as multi-label data. To overcome this challenge, multi-label feature selection is developed. Multi-label feature selection plays an irreplaceable role in pattern recognition and data mining. This process can improve the efficiency and accuracy of multi-label classification. However, traditional multi-label feature selection based on mutual information does not fully consider the effect of redundancy among labels. The deficiency may lead to repeated computing of mutual information and leave room to enhance the accuracy of multi-label feature selection. To deal with this challenge, this paper proposed a multi-label feature selection based on conditional mutual information among labels (CRMIL). Firstly, we analyze how to reduce the redundancy among features based on existing papers. Secondly, we propose a new approach to diminish the redundancy among labels. This method takes label sets as conditions to calculate the relevance between features and labels. This approach can weaken the impact of the redundancy among labels on feature selection results. Finally, we analyze this algorithm and balance the effects of relevance and redundancy on the evaluation function. For testing CRMIL, we compare it with the other eight multi-label feature selection algorithms on ten datasets and use four evaluation criteria to examine the results. Experimental results illustrate that CRMIL performs better than other existing algorithms.

Zhu, Pengfei, Qian Xu, Qinghua Hu, Changqing Zhang, and Hong Zhao. "Multi-label feature selection with missing labels." Pattern Recognition 74 (February 2018): 488–502. http://dx.doi.org/10.1016/j.patcog.2017.09.036.

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Lin, Yaojin, Qinghua Hu, Jia Zhang, and Xindong Wu. "Multi-label feature selection with streaming labels." Information Sciences 372 (December 2016): 256–75. http://dx.doi.org/10.1016/j.ins.2016.08.039.

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Lee, Jaesung, and Dae-Won Kim. "Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection." Entropy 18, no. 11 (November 15, 2016): 405. http://dx.doi.org/10.3390/e18110405.

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Xu, Yuanyuan, Jun Wang, and Jinmao Wei. "To Avoid the Pitfall of Missing Labels in Feature Selection: A Generative Model Gives the Answer." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6534–41. http://dx.doi.org/10.1609/aaai.v34i04.6127.

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In multi-label learning, instances have a large number of noisy and irrelevant features, and each instance is associated with a set of class labels wherein label information is generally incomplete. These missing labels possess two sides like a coin; people cannot predict whether their provided information for feature selection is favorable (relevant) or not (irrelevant) during tossing. Existing approaches either superficially consider the missing labels as negative or indiscreetly impute them with some predicted values, which may either overestimate unobserved labels or introduce new noises in selecting discriminative features. To avoid the pitfall of missing labels, a novel unified framework of selecting discriminative features and modeling incomplete label matrix is proposed from a generative point of view in this paper. Concretely, we relax Smoothness Assumption to infer the label observability, which can reveal the positions of unobserved labels, and employ the spike-and-slab prior to perform feature selection by excluding unobserved labels. Using a data-augmentation strategy leads to full local conjugacy in our model, facilitating simple and efficient Expectation Maximization (EM) algorithm for inference. Quantitative and qualitative experimental results demonstrate the superiority of the proposed approach under various evaluation metrics.

Zhang, Ping, Guixia Liu, Wanfu Gao, and Jiazhi Song. "Multi-label feature selection considering label supplementation." Pattern Recognition 120 (December 2021): 108137. http://dx.doi.org/10.1016/j.patcog.2021.108137.

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Ma, Jianghong, and Tommy W. S. Chow. "Label-specific feature selection and two-level label recovery for multi-label classification with missing labels." Neural Networks 118 (October 2019): 110–26. http://dx.doi.org/10.1016/j.neunet.2019.04.011.

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Дисертації з теми "Label selection":

Jungjit, Suwimol. "New multi-label correlation-based feature selection methods for multi-label classification and application in bioinformatics." Thesis, University of Kent, 2016. https://kar.kent.ac.uk/58873/.

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The very large dimensionality of real world datasets is a challenging problem for classification algorithms, since often many features are redundant or irrelevant for classification. In addition, a very large number of features leads to a high computational time for classification algorithms. Feature selection methods are used to deal with the large dimensionality of data by selecting a relevant feature subset according to an evaluation criterion. The vast majority of research on feature selection involves conventional single-label classification problems, where each instance is assigned a single class label; but there has been growing research on more complex multi-label classification problems, where each instance can be assigned multiple class labels. This thesis proposes three types of new Multi-Label Correlation-based Feature Selection (ML-CFS) methods, namely: (a) methods based on hill-climbing search, (b) methods that exploit biological knowledge (still using hill-climbing search), and (c) methods based on genetic algorithms as the search method. Firstly, we proposed three versions of ML-CFS methods based on hill climbing search. In essence, these ML-CFS versions extend the original CFS method by extending the merit function (which evaluates candidate feature subsets) to the multi-label classification scenario, as well as modifying the merit function in other ways. A conventional search strategy, hill-climbing, was used to explore the space of candidate solutions (candidate feature subsets) for those three versions of ML-CFS. These ML-CFS versions are described in detail in Chapter 4. Secondly, in order to try to improve the performance of ML-CFS in cancer-related microarray gene expression datasets, we proposed three versions of the ML-CFS method that exploit biological knowledge. These ML-CFS versions are also based on hill-climbing search, but the merit function was modified in a way that favours the selection of genes (features) involved in pre-defined cancer-related pathways, as discussed in detail in Chapter 5. Lastly, we proposed two more sophisticated versions of ML-CFS based on Genetic Algorithms (rather than hill-climbing) as the search method. The first version of GA-based ML-CFS is based on a conventional single-objective GA, where there is only one objective to be optimized; while the second version of GA-based ML-CFS performs lexicographic multi-objective optimization, where there are two objectives to be optimized, as discussed in detail in Chapter 6. In this thesis, all proposed ML-CFS methods for multi-label classification problems were evaluated by measuring the predictive accuracies obtained by two well-known multi-label classification algorithms when using the selected featuresม namely: the Multi-Label K-Nearest neighbours (ML-kNN) algorithm and the Multi-Label Back Propagation Multi-Label Learning Neural Network (BPMLL) algorithm. In general, the results obtained by the best version of the proposed ML-CFS methods, namely a GA-based ML-CFS method, were competitive with the results of other multi-label feature selection methods and baseline approaches. More precisely, one of our GA-based methods achieved the second best predictive accuracy out of all methods being compared (both with ML-kNN and BPMLL used as classifiers), but there was no statistically significant difference between that GA-based ML-CFS and the best method in terms of predictive accuracy. In addition, in the experiment with ML-kNN (the most accurate) method selects about twice as many features as our GA-based ML-CFS; whilst in the experiments with BPMLL the most accurate method was a baseline method that does not perform any feature selection, and runs the classifier once (with all original features) for each of the many class labels, which is a very computationally expensive baseline approach. In summary, one of the proposed GA-based ML-CFS methods managed to achieve substantial data reduction, (selecting a smaller subset of relevant features) without a significant decrease in predictive accuracy with respect to the most accurate method.

Gustafsson, Robin. "Ordering Classifier Chains using filter model feature selection techniques." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14817.

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Context: Multi-label classification concerns classification with multi-dimensional output. The Classifier Chain breaks the multi-label problem into multiple binary classification problems, chaining the classifiers to exploit dependencies between labels. Consequently, its performance is influenced by the chain's order. Approaches to finding advantageous chain orders have been proposed, though they are typically costly. Objectives: This study explored the use of filter model feature selection techniques to order Classifier Chains. It examined how feature selection techniques can be adapted to evaluate label dependence, how such information can be used to select a chain order and how this affects the classifier's performance and execution time. Methods: An experiment was performed to evaluate the proposed approach. The two proposed algorithms, Forward-Oriented Chain Selection (FOCS) and Backward-Oriented Chain Selection (BOCS), were tested with three different feature evaluators. 10-fold cross-validation was performed on ten benchmark datasets. Performance was measured in accuracy, 0/1 subset accuracy and Hamming loss. Execution time was measured during chain selection, classifier training and testing. Results: Both proposed algorithms led to improved accuracy and 0/1 subset accuracy (Friedman & Hochberg, p < 0.05). FOCS also improved the Hamming loss while BOCS did not. Measured effect sizes ranged from 0.20 to 1.85 percentage points. Execution time was increased by less than 3 % in most cases. Conclusions: The results showed that the proposed approach can improve the Classifier Chain's performance at a low cost. The improvements appear similar to comparable techniques in magnitude but at a lower cost. It shows that feature selection techniques can be applied to chain ordering, demonstrates the viability of the approach and establishes FOCS and BOCS as alternatives worthy of further consideration.

Sandrock, Trudie. "Multi-label feature selection with application to musical instrument recognition." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019/11071.

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Thesis (PhD)--Stellenbosch University, 2013.
ENGLISH ABSTRACT: An area of data mining and statistics that is currently receiving considerable attention is the field of multi-label learning. Problems in this field are concerned with scenarios where each data case can be associated with a set of labels instead of only one. In this thesis, we review the field of multi-label learning and discuss the lack of suitable benchmark data available for evaluating multi-label algorithms. We propose a technique for simulating multi-label data, which allows good control over different data characteristics and which could be useful for conducting comparative studies in the multi-label field. We also discuss the explosion in data in recent years, and highlight the need for some form of dimension reduction in order to alleviate some of the challenges presented by working with large datasets. Feature (or variable) selection is one way of achieving dimension reduction, and after a brief discussion of different feature selection techniques, we propose a new technique for feature selection in a multi-label context, based on the concept of independent probes. This technique is empirically evaluated by using simulated multi-label data and it is shown to achieve classification accuracy with a reduced set of features similar to that achieved with a full set of features. The proposed technique for feature selection is then also applied to the field of music information retrieval (MIR), specifically the problem of musical instrument recognition. An overview of the field of MIR is given, with particular emphasis on the instrument recognition problem. The particular goal of (polyphonic) musical instrument recognition is to automatically identify the instruments playing simultaneously in an audio clip, which is not a simple task. We specifically consider the case of duets – in other words, where two instruments are playing simultaneously – and approach the problem as a multi-label classification one. In our empirical study, we illustrate the complexity of musical instrument data and again show that our proposed feature selection technique is effective in identifying relevant features and thereby reducing the complexity of the dataset without negatively impacting on performance.
AFRIKAANSE OPSOMMING: ‘n Area van dataontginning en statistiek wat tans baie aandag ontvang, is die veld van multi-etiket leerteorie. Probleme in hierdie veld beskou scenarios waar elke datageval met ‘n stel etikette geassosieer kan word, instede van slegs een. In hierdie skripsie gee ons ‘n oorsig oor die veld van multi-etiket leerteorie en bespreek die gebrek aan geskikte standaard datastelle beskikbaar vir die evaluering van multi-etiket algoritmes. Ons stel ‘n tegniek vir die simulasie van multi-etiket data voor, wat goeie kontrole oor verskillende data eienskappe bied en wat nuttig kan wees om vergelykende studies in die multi-etiket veld uit te voer. Ons bespreek ook die onlangse ontploffing in data, en beklemtoon die behoefte aan ‘n vorm van dimensie reduksie om sommige van die uitdagings wat deur sulke groot datastelle gestel word die hoof te bied. Veranderlike seleksie is een manier van dimensie reduksie, en na ‘n vlugtige bespreking van verskillende veranderlike seleksie tegnieke, stel ons ‘n nuwe tegniek vir veranderlike seleksie in ‘n multi-etiket konteks voor, gebaseer op die konsep van onafhanklike soek-veranderlikes. Hierdie tegniek word empiries ge-evalueer deur die gebruik van gesimuleerde multi-etiket data en daar word gewys dat dieselfde klassifikasie akkuraatheid behaal kan word met ‘n verminderde stel veranderlikes as met die volle stel veranderlikes. Die voorgestelde tegniek vir veranderlike seleksie word ook toegepas in die veld van musiek dataontginning, spesifiek die probleem van die herkenning van musiekinstrumente. ‘n Oorsig van die musiek dataontginning veld word gegee, met spesifieke klem op die herkenning van musiekinstrumente. Die spesifieke doel van (polifoniese) musiekinstrument-herkenning is om instrumente te identifiseer wat saam in ‘n oudiosnit speel. Ons oorweeg spesifiek die geval van duette – met ander woorde, waar twee instrumente saam speel – en hanteer die probleem as ‘n multi-etiket klassifikasie een. In ons empiriese studie illustreer ons die kompleksiteit van musiekinstrumentdata en wys weereens dat ons voorgestelde veranderlike seleksie tegniek effektief daarin slaag om relevante veranderlikes te identifiseer en sodoende die kompleksiteit van die datastel te verminder sonder ‘n negatiewe impak op klassifikasie akkuraatheid.

Paredes, Zevallos Daniel Leoncio. "Multi-scale image inpainting with label selection based on local statistics." Master's thesis, Pontificia Universidad Católica del Perú, 2014. http://tesis.pucp.edu.pe/repositorio/handle/123456789/5578.

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We proposed a novel inpainting method where we use a multi-scale approach to speed up the well-known Markov Random Field (MRF) based inpainting method. MRF based inpainting methods are slow when compared with other exemplar-based methods, because its computational complexity is O(jLj2) (L feasible solutions’ labels). Our multi-scale approach seeks to reduces the number of the L (feasible) labels by an appropiate selection of the labels using the information of the previous (low resolution) scale. For the initial label selection we use local statistics; moreover, to compensate the loss of information in low resolution levels we use features related to the original image gradient. Our computational results show that our approach is competitive, in terms reconstruction quality, when compare to the original MRF based inpainting, as well as other exemplarbased inpaiting algorithms, while being at least one order of magnitude faster than the original MRF based inpainting and competitive with exemplar-based inpaiting.
Tesis

Duncan, Alyssa Renee. ""Nutrition facts" label use in the selection of healthier foods by undergraduate students." FIU Digital Commons, 1996. http://digitalcommons.fiu.edu/etd/3239.

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Use of "Nutrition Facts" panel on food labels was studied in the selection of healthier substitutes for foods normally consumed by 276 undergraduates, mean age, 19.7+2.5 years. Among 1095 label pairs (3.97 per student), 80.6% included a "healthier" substitute. Most common food categories were cookies/bars/tarts (12.8%), cereal (11.8%), chips/crackers (11.1%), beverages (10.2%) and breads/muffins (9.1%). Up to three errors were recorded per label pair, with 384 total errors made, including failure to adjust for serving size (34%), use of pre-NLEA labels (30%), comparison of unlike foods (16%) and unclear comparisons or missing labels (19%). Among 3295 nutrient comparisons, total fat (23.6%), calories (18.4%) and sodium (11.7%) were cited most often. Substitutes were a little (1-10% difference) to a lot healthier (>51% difference) for 83% of nutrients. Sixty percent would purchase healthier foods again or look for other substitutes and 47% stated they preferred the substitute's taste or thought it equivalent.

Gonzalez, Lopez Jorge. "Distributed multi-label learning on Apache Spark." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5775.

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This thesis proposes a series of multi-label learning algorithms for classification and feature selection implemented on the Apache Spark distributed computing model. Five approaches for determining the optimal architecture to speed up multi-label learning methods are presented. These approaches range from local parallelization using threads to distributed computing using independent or shared memory spaces. It is shown that the optimal approach performs hundreds of times faster than the baseline method. Three distributed multi-label k nearest neighbors methods built on top of the Spark architecture are proposed: an exact iterative method that computes pair-wise distances, an approximate tree-based method that indexes the instances across multiple nodes, and an approximate local sensitive hashing method that builds multiple hash tables to index the data. The results indicated that the predictions of the tree-based method are on par with those of an exact method while reducing the execution times in all the scenarios. The aforementioned method is then used to evaluate the quality of a selected feature subset. The optimal adaptation for a multi-label feature selection criterion is discussed and two distributed feature selection methods for multi-label problems are proposed: a method that selects the feature subset that maximizes the Euclidean norm of individual information measures, and a method that selects the subset of features maximizing the geometric mean. The results indicate that each method excels in different scenarios depending on type of features and the number of labels. Rigorous experimental studies and statistical analyses over many multi-label metrics and datasets confirm that the proposals achieve better performances and provide better scalability to bigger data than the methods compared in the state of the art.

Lu, Tien-hsin. "SqueezeFit Linear Program: Fast and Robust Label-aware Dimensionality Reduction." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587156777565173.

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Gharroudi, Ouadie. "Ensemble multi-label learning in supervised and semi-supervised settings." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1333/document.

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L'apprentissage multi-label est un problème d'apprentissage supervisé où chaque instance peut être associée à plusieurs labels cibles simultanément. Il est omniprésent dans l'apprentissage automatique et apparaÃ®t naturellement dans de nombreuses applications du monde réel telles que la classification de documents, l'étiquetage automatique de musique et l'annotation d'images. Nous discutons d'abord pourquoi les algorithmes multi-label de l'etat-de-l'art utilisant un comité de modèle souffrent de certains inconvénients pratiques. Nous proposons ensuite une nouvelle stratégie pour construire et agréger les modèles ensemblistes multi-label basés sur k-labels. Nous analysons ensuite en profondeur l'effet de l'étape d'agrégation au sein des approches ensemblistes multi-label et étudions comment cette agrégation influece les performances de prédictive du modèle enfocntion de la nature de fonction cout à optimiser. Nous abordons ensuite le problème spécifique de la selection de variables dans le contexte multi-label en se basant sur le paradigme ensembliste. Trois méthodes de sélection de caractéristiques multi-label basées sur le paradigme des forêts aléatoires sont proposées. Ces méthodes diffèrent dans la façon dont elles considèrent la dépendance entre les labels dans le processus de sélection des varibales. Enfin, nous étendons les problèmes de classification et de sélection de variables au cadre d'apprentissage semi-supervisé. Nous proposons une nouvelle approche de sélection de variables multi-label semi-supervisée basée sur le paradigme de l'ensemble. Le modèle proposé associe des principes issues de la co-training en conjonction avec une métrique interne d'évaluation d'importnance des varaibles basée sur les out-of-bag. Testés de manière satisfaisante sur plusieurs données de référence, les approches développées dans cette thèse sont prometteuses pour une variété d'ap-plications dans l'apprentissage multi-label supervisé et semi-supervisé. Testés de manière satisfaisante sur plusieurs jeux de données de référence, les approches développées dans cette thèse affichent des résultats prometteurs pour une variété domaine d'applications de l'apprentissage multi-label supervisé et semi-supervisé
Multi-label learning is a specific supervised learning problem where each instance can be associated with multiple target labels simultaneously. Multi-label learning is ubiquitous in machine learning and arises naturally in many real-world applications such as document classification, automatic music tagging and image annotation. In this thesis, we formulate the multi-label learning as an ensemble learning problem in order to provide satisfactory solutions for both the multi-label classification and the feature selection tasks, while being consistent with respect to any type of objective loss function. We first discuss why the state-of-the art single multi-label algorithms using an effective committee of multi-label models suffer from certain practical drawbacks. We then propose a novel strategy to build and aggregate k-labelsets based committee in the context of ensemble multi-label classification. We then analyze the effect of the aggregation step within ensemble multi-label approaches in depth and investigate how this aggregation impacts the prediction performances with respect to the objective multi-label loss metric. We then address the specific problem of identifying relevant subsets of features - among potentially irrelevant and redundant features - in the multi-label context based on the ensemble paradigm. Three wrapper multi-label feature selection methods based on the Random Forest paradigm are proposed. These methods differ in the way they consider label dependence within the feature selection process. Finally, we extend the multi-label classification and feature selection problems to the semi-supervised setting and consider the situation where only few labelled instances are available. We propose a new semi-supervised multi-label feature selection approach based on the ensemble paradigm. The proposed model combines ideas from co-training and multi-label k-labelsets committee construction in tandem with an inner out-of-bag label feature importance evaluation. Satisfactorily tested on several benchmark data, the approaches developed in this thesis show promise for a variety of applications in supervised and semi-supervised multi-label learning

Narassiguin, Anil. "Apprentissage Ensembliste, Étude comparative et Améliorations via Sélection Dynamique." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE1075/document.

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Les méthodes ensemblistes constituent un sujet de recherche très populaire au cours de la dernière décennie. Leur succès découle en grande partie de leurs solutions attrayantes pour résoudre différents problèmes d'apprentissage intéressants parmi lesquels l'amélioration de l'exactitude d'une prédiction, la sélection de variables, l'apprentissage de métrique, le passage à l'échelle d'algorithmes inductifs, l'apprentissage de multiples jeux de données physiques distribués, l'apprentissage de flux de données soumis à une dérive conceptuelle, etc... Dans cette thèse nous allons dans un premier temps présenter une comparaison empirique approfondie de 19 algorithmes ensemblistes d'apprentissage supervisé proposé dans la littérature sur différents jeux de données de référence. Non seulement nous allons comparer leurs performances selon des métriques standards de performances (Exactitude, AUC, RMS) mais également nous analyserons leur diagrammes kappa-erreur, la calibration et les propriétés biais-variance. Nous allons aborder ensuite la problématique d'amélioration des ensembles de modèles par la sélection dynamique d'ensembles (dynamic ensemble selection, DES). La sélection dynamique est un sous-domaine de l'apprentissage ensembliste où pour une donnée d'entrée x, le meilleur sous-ensemble en terme de taux de réussite est sélectionné dynamiquement. L'idée derrière les approches DES est que différents modèles ont différentes zones de compétence dans l'espace des instances. La plupart des méthodes proposées estime l'importance individuelle de chaque classifieur faible au sein d'une zone de compétence habituellement déterminée par les plus proches voisins dans un espace euclidien. Nous proposons et étudions dans cette thèse deux nouvelles approches DES. La première nommée ST-DES est conçue pour les ensembles de modèles à base d'arbres de décision. Cette méthode sélectionne via une métrique supervisée interne à l'arbre, idée motivée par le problème de la malédiction de la dimensionnalité : pour les jeux de données avec un grand nombre de variables, les métriques usuelles telle la distance euclidienne sont moins pertinentes. La seconde approche, PCC-DES, formule la problématique DES en une tâche d'apprentissage multi-label avec une fonction coût spécifique. Ici chaque label correspond à un classifieur et une base multi-label d'entraînement est constituée sur l'habilité de chaque classifieur de classer chaque instance du jeu de données d'origine. Cela nous permet d'exploiter des récentes avancées dans le domaine de l'apprentissage multi-label. PCC-DES peut être utilisé pour les approches ensemblistes homogènes et également hétérogènes. Son avantage est de prendre en compte explicitement les corrélations entre les prédictions des classifieurs. Ces algorithmes sont testés sur un éventail de jeux de données de référence et les résultats démontrent leur efficacité faces aux dernières alternatives de l'état de l'art
Ensemble methods has been a very popular research topic during the last decade. Their success arises largely from the fact that they offer an appealing solution to several interesting learning problems, such as improving prediction accuracy, feature selection, metric learning, scaling inductive algorithms to large databases, learning from multiple physically distributed data sets, learning from concept-drifting data streams etc. In this thesis, we first present an extensive empirical comparison between nineteen prototypical supervised ensemble learning algorithms, that have been proposed in the literature, on various benchmark data sets. We not only compare their performance in terms of standard performance metrics (Accuracy, AUC, RMS) but we also analyze their kappa-error diagrams, calibration and bias-variance properties. We then address the problem of improving the performances of ensemble learning approaches with dynamic ensemble selection (DES). Dynamic pruning is the problem of finding given an input x, a subset of models among the ensemble that achieves the best possible prediction accuracy. The idea behind DES approaches is that different models have different areas of expertise in the instance space. Most methods proposed for this purpose estimate the individual relevance of the base classifiers within a local region of competence usually given by the nearest neighbours in the euclidean space. We propose and discuss two novel DES approaches. The first, called ST-DES, is designed for decision tree based ensemble models. This method prunes the trees using an internal supervised tree-based metric; it is motivated by the fact that in high dimensional data sets, usual metrics like euclidean distance suffer from the curse of dimensionality. The second approach, called PCC-DES, formulates the DES problem as a multi-label learning task with a specific loss function. Labels correspond to the base classifiers and multi-label training examples are formed based on the ability of each classifier to correctly classify each original training example. This allows us to take advantage of recent advances in the area of multi-label learning. PCC-DES works on homogeneous and heterogeneous ensembles as well. Its advantage is to explicitly capture the dependencies between the classifiers predictions. These algorithms are tested on a variety of benchmark data sets and the results demonstrate their effectiveness against competitive state-of-the-art alternatives

Kraus, Vivien. "Apprentissage semi-supervisé pour la régression multi-labels : application à l’annotation automatique de pneumatiques." Thesis, Lyon, 2021. https://tel.archives-ouvertes.fr/tel-03789608.

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Avec l’avènement et le développement rapide des technologies numériques, les données sont devenues à la fois un bien précieux et très abondant. Cependant, avec une telle profusion, se posent des questions relatives à la qualité et l’étiquetage de ces données. En effet, à cause de l’augmentation des volumes de données disponibles, alors que le coût de l’étiquetage par des experts humains reste très important, il est de plus en plus nécessaire de pouvoir renforcer l’apprentissage semi-supervisé grâce l’exploitation des données nonlabellisées. Ce problème est d’autant plus marqué dans le cas de l’apprentissage multilabels, et en particulier pour la régression, où chaque unité statistique est guidée par plusieurs cibles différentes, qui prennent la forme de scores numériques. C’est dans ce cadre fondamental, que s’inscrit cette thèse. Tout d’abord, nous commençons par proposer une méthode d’apprentissage pour la régression semi-supervisée, que nous mettons à l’épreuve à travers une étude expérimentale détaillée. Grâce à cette nouvelle méthode, nous présentons une deuxième contribution, plus adaptée au contexte multi-labels. Nous montrons également son efficacité par une étude comparative, sur des jeux de données issues de la littérature. Par ailleurs, la dimensionnalité du problème demeure toujours la difficulté de l’apprentissage automatique, et sa réduction suscite l’intérêt de plusieurs chercheurs dans la communauté. Une des tâches majeures répondant à cette problématique est la sélection de variables, que nous proposons d’étudier ici dans un cadre complexe : semi-supervisé, multi-labels et pour la régression
With the advent and rapid growth of digital technologies, data has become a precious asset as well as plentiful. However, with such an abundance come issues about data quality and labelling. Because of growing numbers of available data volumes, while human expert labelling is still important, it is more and more necessary to reinforce semi-supervised learning with the exploitation of unlabeled data. This problem is all the more noticeable in the multi-label learning framework, and in particular for regression, where each statistical unit is guided by many different targets, taking the form of numerical scores. This thesis focuses on this fundamental framework. First, we begin by proposing a method for semi-supervised regression, that we challenge through a detailed experimental study. Thanks to this new method, we present a second contribution, more fitted to the multi-label framework. We also show its efficiency with a comparative study on literature data sets. Furthermore, the problem dimension is always a pain point of machine learning, and reducing it sparks the interest of many researchers. Feature selection is one of the major tasks addressing this problem, and we propose to study it here in a complex framework : for semi-supervised, multi-label regression. Finally, an experimental validation is proposed on a real problem about automatic annotation of tires, to tackle the needs expressed by the industrial partner of this thesis

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Книги з теми "Label selection":

Emmerling, Sonja. Hadamar von Laber und seine Liebesdichtung "Die Jagd". Regensburg: Schnell + Steiner, 2005.

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United States. Congress. Senate. Committee on Finance. Nominations of Dr. Lael Brainard, Mary John Miller, and Charles Collyns: Hearing before the Committee on Finance, United States Senate, One Hundred Eleventh Congress, first session, on the nominations of Dr. Lael Brainard, to be Under Secretary of the Treasury for International Affairs; Mary John Miller, to be Assistant Secretary of the Treasury for Financial Markets; and Charles Collyns, to be Assistant Secretary of the Treasury for International Finance, November 20, 2009. Washington: U.S. G.P.O., 2009.

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United States. Congress. Senate. Committee on Banking, Housing, and Urban Affairs. Nominations of: Stanley Fischer, Jerome H. Powell, Lael Brainard, Gustavo Velasquez Aguilar, and J. Mark McWatters: Hearing before the Committee on Banking, Housing, and Urban Affairs, United States Senate, One Hundred Thirteenth Congress, second session, on nominations of: Stanley Fischer, to be a member and vice chairman of the Board of Governors of the Federal Reserve System; Jerome H. Powell, to be a member of the Board of Governors of the Federal Reserve System; Lael Brainard, to be a member of the Board of Governors of the Federal Reserve System; Gustavo Velasquez Aguilar, to be an assistant secretary of the Department of Housing and Urban Development; J. Mark McWatters, to be a member of the National Credit Union Administration Board, March 13, 2014. Washington: U.S. Government Printing Office, 2014.

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Bucher, Gina. Female Chic : Thema Selection: The Story of a Zu?rich Fashion Label. Frey Edition im Verlag der Alltag, Patrick, 2016.

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Burford, Mark. Conclusion. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190634902.003.0012.

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IN DECEMBER 1951, Columbia Records released a four-LP set produced by George Avakian, The Bessie Smith Story, offering a curated selection of remastered recordings that the great blues singer made for the label in the 1920s and 1930s. “To those who knew her and were familiar with her work,” ...

Guerdjikova, Anna I., Paul E. Keck, and Susan L. McElroy. The impact of psychiatric co-morbidity in the treatment of bipolar disorder: focus on co-occurring attention deficit hyperactivity disorder and eating disorders. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198748625.003.0018.

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Bipolar disorder (BD) commonly co-occurs with attention deficit hyperactivity disorder (ADHD) and eating disorders (EDs) in adolescents and in adults. The aim of this chapter is to summarize the available data regarding prevalence, clinical presentation, and psychological and pharmacological treatment of such complicated cases. Results of randomized controlled and open-label trials and case reports are reviewed. The main therapeutic goal when treating BD co-morbid with ADHD or ED is selecting a treatment strategy effective in the management of both syndromes, or at the minimum, selecting one that treats one syndrome without exacerbating the other. Controlled data are scarce. Various classes of medications, including stimulants, atomoxetine, bupropion, and wakefulness-provoking agents, might hold promise as adjunctive medication in improving ADHD symptoms in euthymic BD patients. The specificities of the ED, namely the predominance of undereating or overeating, need to be considered when selecting agents in the treatment of BD co-morbid with EDs.

Kleppinger, Kathryn A. Branding the 'Beur' Author. Liverpool University Press, 2016. http://dx.doi.org/10.5949/liverpool/9781781381960.001.0001.

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Branding the Beur Author analyzes mainstream media promotion of literature written by the descendants of North African immigrants to France (often called beurs). Launched in the early 1980s, conversations between journalists and ‘beur’ authors delve into contemporary debates such as racism in the 1980s and Islam in French society in the 1990s. But the interests of journalists looking for sensational subject matter also heavily shape the promotion and reception of these novels: only the ‘beur’ authors who use a realist style to write about the challenges faced by the North African immigrant population in France—and who engage on-air with French identity politics and immigration—receive multiple invitations to participate in interviews. Previous scholarship has taken a necessary first step by analyzing the social and political stakes of this literature (using labels such as ‘beur’ and/or ‘banlieue,’ to designate its urban, economically distressed setting), but this book argues that this approach reproduces the selection criteria deployed by the media that determine which texts receive commercial and critical support. By demonstrating how minority-based literary labels such as ‘francophone’ and ‘postcolonial’ are always already defined by the socio-political context in which such works are published and promoted, this book establishes that these labels are tautological and cannot reflect the thematic and stylistic richness of beur (and other minority) production in France.

Urrieta, Luis. Cultural Identity Theory and Education. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190676087.003.0001.

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This chapter presents a selective overview of the study of identity. Identity is defined broadly as self-understandings, especially those with strong emotional resonances, and often marked with socially constructed raced, gendered, classed, and sexual identity labels. The definition of identity is based on two assumptions: (a) the study of identity is the study of subject formation and (b) identity is about power. The chapter then proceeds to address two aspects of cultural identity as a concept: first, the power that cultural identity has for identity politics, followed by the political dimensions of cultural identity as used by oppressed and minoritized groups in social movements and activism, especially those related to education. The chapter then focuses on the relevance of identity to address difference in education and concludes with asserting the importance of qualitative research in the study of identity.

Частини книг з теми "Label selection":

Mansouri, Dou El Kefel, and Khalid Benabdeslem. "Towards Multi-label Feature Selection by Instance and Label Selections." In Advances in Knowledge Discovery and Data Mining, 233–44. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75765-6_19.

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Yan, Yan, Shining Li, Zhe Yang, Xiao Zhang, Jing Li, Anyi Wang, and Jingyu Zhang. "Multi-label Learning with Label-Specific Feature Selection." In Neural Information Processing, 305–15. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_33.

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Xu, Qian, Pengfei Zhu, Qinghua Hu, and Changqing Zhang. "Robust Multi-label Feature Selection with Missing Labels." In Communications in Computer and Information Science, 752–65. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3002-4_61.

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Jiao, Yang, Pengpeng Zhao, Jian Wu, Xuefeng Xian, Haihui Xu, and Zhiming Cui. "Active Multi-label Learning with Optimal Label Subset Selection." In Advanced Data Mining and Applications, 523–34. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14717-8_41.

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Peng, Tao, Jun Li, and Jianhua Xu. "Label Selection Algorithm Based on Iteration Column Subset Selection for Multi-label Classification." In Lecture Notes in Computer Science, 287–301. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12423-5_22.

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Ji, Tianqi, Jun Li, and Jianhua Xu. "Label Selection Algorithm Based on Boolean Interpolative Decomposition with Sequential Backward Selection for Multi-label Classification." In Document Analysis and Recognition – ICDAR 2021, 130–44. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86331-9_9.

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Li, Ling, Huawen Liu, Zongjie Ma, Yuchang Mo, Zhengjie Duan, Jiaqing Zhou, and Jianmin Zhao. "Multi-label Feature Selection via Information Gain." In Advanced Data Mining and Applications, 345–55. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14717-8_27.

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Doquire, Gauthier, and Michel Verleysen. "Feature Selection for Multi-label Classification Problems." In Advances in Computational Intelligence, 9–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21501-8_2.

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Pillai, Ignazio, Giorgio Fumera, and Fabio Roli. "Classifier Selection Approaches for Multi-label Problems." In Multiple Classifier Systems, 167–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21557-5_19.

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Montejo-Ráez, Arturo, and Luis Alfonso Ureña-López. "Selection Strategies for Multi-label Text Categorization." In Advances in Natural Language Processing, 585–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11816508_58.

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Тези доповідей конференцій з теми "Label selection":

Yan, Yi-Fan, and Sheng-Jun Huang. "Cost-Effective Active Learning for Hierarchical Multi-Label Classification." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/411.

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Active learning reduces the labeling cost by actively querying labels for the most valuable data. It is particularly important for multi-label learning, where the annotation cost is rather high because each instance may have multiple labels simultaneously. In many multi-label tasks, the labels are organized into hierarchies from coarse to fine. The labels at different levels of the hierarchy contribute differently to the model training, and also have diverse annotation costs. In this paper, we propose a multi-label active learning approach to exploit the label hierarchies for cost-effective queries. By incorporating the potential contribution of ancestor and descendant labels, a novel criterion is proposed to estimate the informativeness of each candidate query. Further, a subset selection method is introduced to perform active batch selection by balancing the informativeness and cost of each instance-label pair. Experimental results validate the effectiveness of both the proposed criterion and the selection method.

Ren, Tingting, Xiuyi Jia, Weiwei Li, Lei Chen, and Zechao Li. "Label distribution learning with label-specific features." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/460.

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Label distribution learning (LDL) is a novel machine learning paradigm to deal with label ambiguity issues by placing more emphasis on how relevant each label is to a particular instance. Many LDL algorithms have been proposed and most of them concentrate on the learning models, while few of them focus on the feature selection problem. All existing LDL models are built on a simple feature space in which all features are shared by all the class labels. However, this kind of traditional data representation strategy tends to select features that are distinguishable for all labels, but ignores label-specific features that are pertinent and discriminative for each class label. In this paper, we propose a novel LDL algorithm by leveraging label-specific features. The common features for all labels and specific features for each label are simultaneously learned to enhance the LDL model. Moreover, we also exploit the label correlations in the proposed LDL model. The experimental results on several real-world data sets validate the effectiveness of our method.

Zhang, Jia, Yidong Lin, Min Jiang, Shaozi Li, Yong Tang, and Kay Chen Tan. "Multi-label Feature Selection via Global Relevance and Redundancy Optimization." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/348.

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Information theoretical based methods have attracted a great attention in recent years, and gained promising results to deal with multi-label data with high dimensionality. However, most of the existing methods are either directly transformed from heuristic single-label feature selection methods or inefficient in exploiting labeling information. Thus, they may not be able to get an optimal feature selection result shared by multiple labels. In this paper, we propose a general global optimization framework, in which feature relevance, label relevance (i.e., label correlation), and feature redundancy are taken into account, thus facilitating multi-label feature selection. Moreover, the proposed method has an excellent mechanism for utilizing inherent properties of multi-label learning. Specially, we provide a formulation to extend the proposed method with label-specific features. Empirical studies on twenty multi-label data sets reveal the effectiveness and efficiency of the proposed method. Our implementation of the proposed method is available online at: https://jiazhang-ml.pub/GRRO-master.zip.

Spolaor, Newton, Maria Carolina Monard, Grigorios Tsoumakas, and Huei Lee. "Label Construction for Multi-label Feature Selection." In 2014 Brazilian Conference on Intelligent Systems (BRACIS). IEEE, 2014. http://dx.doi.org/10.1109/bracis.2014.52.

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Li, Weiwei, Jin Chen, Yuqing Lu, and Zhiqiu Huang. "Filling Missing Labels in Label Distribution Learning by Exploiting Label-Specific Feature Selection." In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. http://dx.doi.org/10.1109/ijcnn55064.2022.9892220.

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Yin, Zhijian, Xingxing Li, and Hualin Zhan. "Multi-label Feature Selection based on Label-specific Features." In 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC). IEEE, 2019. http://dx.doi.org/10.1109/sdpc.2019.00137.

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Langerak, T. R., U. A. van der Heide, I. M. Lips, A. N. T. J. Kotte, M. van Vulpen, and J. P. W. Pluim. "Label fusion using performance estimation with iterative label selection." In 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI). IEEE, 2009. http://dx.doi.org/10.1109/isbi.2009.5193270.

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Gu, Quanquan, Zhenhui Li, and Jiawei Han. "Correlated multi-label feature selection." In the 20th ACM international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2063576.2063734.

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Wang, Jing, Peipei Li, and Kui Yu. "Partial Multi-Label Feature Selection." In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. http://dx.doi.org/10.1109/ijcnn55064.2022.9892133.

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Lyu, Gengyu, Yanan Wu, and Songhe Feng. "Deep Graph Matching for Partial Label Learning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/459.

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Partial Label Learning (PLL) aims to learn from training data where each instance is associated with a set of candidate labels, among which only one is correct. In this paper, we formulate the task of PLL problem as an ``instance-label'' matching selection problem, and propose a DeepGNN-based graph matching PLL approach to solve it. Specifically, we first construct all instances and labels as graph nodes into two different graphs respectively, and then integrate them into a unified matching graph by connecting each instance to its candidate labels. Afterwards, the graph attention mechanism is adopted to aggregate and update all nodes state on the instance graph to form structural representations for each instance. Finally, each candidate label is embedded into its corresponding instance and derives a matching affinity score for each instance-label correspondence with a progressive cross-entropy loss. Extensive experiments on various data sets have demonstrated the superiority of our proposed method.

Звіти організацій з теми "Label selection":

Atlas, A., J. Drake, S. Giacalone, and S. Previdi. Performance-Based Path Selection for Explicitly Routed Label Switched Paths (LSPs) Using TE Metric Extensions. RFC Editor, May 2016. http://dx.doi.org/10.17487/rfc7823.

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Gehlen, J. R. Function Selection with the Tablet: The Effect of Labels for Visual Cuing. Fort Belvoir, VA: Defense Technical Information Center, February 1988. http://dx.doi.org/10.21236/ada198229.

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Wong, Stephen T., Xiaoyun Xu, Zhengfan Liu, Xu Chen, Zachary Satira, and Xi Wang. A Label-Free and Chemical-Selective Microendoscope to Enhance Prostate Cancer Surgical Outcomes. Fort Belvoir, VA: Defense Technical Information Center, October 2013. http://dx.doi.org/10.21236/ada600022.

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Li, Y., D. Eastlake, W. Hao, H. Chen, and S. Chatterjee. Transparent Interconnection of Lots of Links (TRILL): Using Data Labels for Tree Selection for Multi-Destination Data. RFC Editor, August 2016. http://dx.doi.org/10.17487/rfc7968.

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Sullivan, A. Selecting Labels for Use with Conventional DNS and Other Resolution Systems in DNS-Based Service Discovery. RFC Editor, September 2017. http://dx.doi.org/10.17487/rfc8222.

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Fluhr, Robert, and Maor Bar-Peled. Novel Lectin Controls Wound-responses in Arabidopsis. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7697123.bard.

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Innate immune responses in animals and plants involve receptors that recognize microbe-associated molecules. In plants, one set of this defense system is characterized by large families of TIR–nucleotide binding site–leucine-rich repeat (TIR-NBS-LRR) resistance genes. The direct interaction between plant proteins harboring the TIR domain with proteins that transmit and facilitate a signaling pathway has yet to be shown. The Arabidopsis genome encodes TIR-domain containing genes that lack NBS and LRR whose functions are unknown. Here we investigated the functional role of such protein, TLW1 (TIR LECTIN WOUNDRESPONSIVE1). The TLW1 gene encodes a protein with two domains: a TIR domain linked to a lectin-containing domain. Our specific aim in this proposal was to examine the ramifications of the TL1-glycan interaction by; A) The functional characterization of TL1 activity in the context of plant wound response and B) Examine the hypothesis that wounding induced specific polysaccharides and examine them as candidates for TL-1 interactive glycan compounds. The Weizmann group showed TLW1 transcripts are rapidly induced by wounding in a JA-independent pathway and T-DNA-tagged tlw1 mutants that lack TLW1 transcripts, fail to initiate the full systemic wound response. Transcriptome methodology analysis was set up and transcriptome analyses indicates a two-fold reduced level of JA-responsive but not JA-independent transcripts. The TIR domain of TLW1 was found to interact directly with the KAT2/PED1 gene product responsible for the final b-oxidation steps in peroxisomal-basedJA biosynthesis. To identify potential binding target(s) of TL1 in plant wound response, the CCRC group first expressed recombinant TL1 in bacterial cells and optimized conditions for the protein expression. TL1 was most highly expressed in ArcticExpress cell line. Different types of extraction buffers and extraction methods were used to prepare plant extracts for TL1 binding assay. Optimized condition for glycan labeling was determined, and 2-aminobenzamide was used to label plant extracts. Sensitivity of MALDI and LC-MS using standard glycans. THAP (2,4,6- Trihydroxyacetophenone) showed minimal background peaks at positive mode of MALDI, however, it was insensitive with a minimum detection level of 100 ng. Using LC-MS, sensitivity was highly increased enough to detect 30 pmol concentration. However, patterns of total glycans displayed no significant difference between different extraction conditions when samples were separated with Dionex ICS-2000 ion chromatography system. Transgenic plants over-expressing lectin domains were generated to obtain active lectin domain in plant cells. Insertion of the overexpression construct into the plant genome was confirmed by antibiotic selection and genomic DNA PCR. However, RT-PCR analysis was not able to detect increased level of the transcripts. Binding ability of azelaic acid to recombinant TL1. Azelaic acid was detected in GST-TL1 elution fraction, however, DHB matrix has the same mass in background signals, which needs to be further tested on other matrices. The major findings showed the importance of TLW1 in regulating wound response. The findings demonstrate completely novel and unexpected TIR domain interactions and reveal a control nexus and mechanism that contributes to the propagation of wound responses in Arabidopsis. The implications are to our understanding of the function of TIR domains and to the notion that early molecular events occur systemically within minutes of a plant sustaining a wound. A WEB site (http://genome.weizmann.ac.il/hormonometer/) was set up that enables scientists to interact with a collated plant hormone database.

Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0616.

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As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of extracting signal components. Therefore, a method based on spectral energy distribution is proposed to determine the parameter, and the quadratic penalty term is optimized according to SNR. The results show that the optimized VMD can adaptively extract the vibration signal components of the diesel engine. In the actual fault diagnosis case, it is difficult to obtain the data with labels. The clustering algorithm can complete the classification without labeled data, but it is limited by the low accuracy. In this paper, the optimized VMD is used to decompose and standardize the vibration signal. Then the correlation-based feature selection method is implemented to obtain the feature results after dimensionality reduction. Finally, the results are input into the classifier combined by K-means and genetic algorithm (GA). By introducing and optimizing the genetic algorithm, the number of classes can be selected automatically, and the accuracy is significantly improved. This method can carry out adaptive multiple fault detection of a diesel engine without labeled data. Compared with many supervised learning algorithms, the proposed method also has high accuracy.