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Makkhongkaew R.,University of Lyon1 | Benabdeslem K.,University of Lyon1
IEEE International Conference on Data Mining Workshops, ICDMW | Year: 2017

Semi-supervised learning is the required paradigm when data are partially labeled. It is more adapted for large domain applications when labels are hardly and costly to obtain. In addition, when data are large, feature selection and instance selection are two important dual operations for removing irrelevant information. To address theses challenges together, we propose a unified framework, called sCOs, for semi-supervised co-selection of features and instances, simultaneously. In particular, we propose a novel cost function based on l2, 1-norm regularization and similarity preserving selection of both features and instances. Experimental results on some known benchmark datasets are provided for validating sCOs and comparing it with some representative methods in the state-of-The art. © 2016 IEEE.


Chen Y.,University of Lyon1 | Chen Y.,Institut Universitaire de France | Mishra S.,University of Lyon1 | Ledoux G.,Institut Universitaire de France | And 3 more authors.
Chemistry - An Asian Journal | Year: 2014

A novel single-source precursor NaGd(TFA)4(diglyme) (TFA=trifluoroacetate) was synthesized, characterized thoroughly, and used to obtain the hexagonal phase of NaGdF4 nanoparticles as an efficient matrix for lanthanide-doped upconverting nanocrystals (NCs) that convert near-infrared radiation into shorter-wavelength UV/visible light. These NCs were then used to prepare well-characterized TiO2@NaGdF 4:Yb3+,Tm3+ nanocomposites to extend the absorption range of the TiO2 photocatalyst from the UV to the IR region. While the visible/near IR part of the photoluminescent spectra remains almost unaffected by the presence of TiO2, the UV part is strongly quenched due to the absorption of TiO2 above its gap at approximately 380 nm by energy transfer or FRET. Preliminary results on the photocatalytic activity of the above obtained nanocomposites are presented. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.


Pugeat M.,Groupement Hospitalier Est | Pugeat M.,French Institute of Health and Medical Research | Pugeat M.,University Of Lyon1 | Nader N.,Eunice Kennedy Shriver National Institute of Child Health and Human Development | And 9 more authors.
Molecular and Cellular Endocrinology | Year: 2010

Sex hormone-binding globulin (SHBG) is the main transport binding protein for sex steroid hormones in plasma and regulates their accessibility to target cells. Plasma SHBG is secreted by the liver under the control of hormones and nutritional factors. In the human hepatoma cell line (HepG2), thyroid and estrogenic hormones, and a variety of drugs including the antioestrogen tamoxifen, the phytoestrogen, genistein and mitotane (Op′DDD) increase SHBG production and SHBG gene promoter activity. In contrast, monosaccharides (glucose or fructose) effectively decrease SHBG expression by inducing lipogenesis, which reduces hepatic HNF-4α levels, a transcription factor that play a critical role in controlling the SHBG promoter. Interestingly, diminishing hepatic lipogenesis and free fatty acid liver biosynthesis also appear to be associated with the positive effects of thyroid hormones and PPARγ antagonists on SHBG expression. This mechanism provides a biological explanation for why SHBG is a sensitive biomarker of insulin resistance and the metabolic syndrome, and why low plasma SHBG levels are a risk factor for developing hyperglycemia and type 2 diabetes, especially in women. These important advances in our knowledge of the regulation of SHBG expression in the liver open new approaches for identifying and preventing metabolic disorder-associated diseases early in life. © 2009 Elsevier Ireland Ltd.


Mishra S.,French National Center for Scientific Research | Mishra S.,University of Lyon1 | Jeanneau E.,University of Lyon1 | Bulin A.-L.,French National Center for Scientific Research | And 6 more authors.
Dalton Transactions | Year: 2013

A series of anhydrous cerium(iii) trifluoroacetate complexes with neutral O-donor ligands, namely Ce2(OAc)(TFA)5(DMF)3 (1), Ce(TFA)3(L)x [x = 2, L = THF (2), DMF (3), DMSO (4); x = 1, L = diglyme (5)] and Ce2(TFA)6(DMSO) x(DMF)y [x = 6, y = 0 (6); x = 4, y = 2 (7)] (where OAc = acetate, TFA = trifluoroacetate, THF = tetrahydrofuran, DMF = dimethylformamide, DMSO = dimethylsulphoxide, and diglyme = MeO(C2H4O) 2Me] were synthesized and completely characterized by elemental analysis, FT-IR spectroscopy and TG-DTA-MS studies. A partially hydrated complex [Ce(TFA)3(diglyme)(H2O)] (8) was obtained by slow evaporation of the THF solution of anhydrous 5 in the air. The single crystal X-ray diffraction analysis of 1, 3, 4, and 6-8 showed the versatile bonding mode of the TFA ligand (terminal, chelating and bridging). These complexes, on decomposition in 1-octadecene in inert atmosphere, gave CeF3 nanoparticles of 8-11 nm size. The complex 5 proved to be the best precursor in the series because of the ability of the diglyme ligand to act as capping reagent during decomposition to render the CeF3 particles monodisperse in organic solvents. The obtained CeF3 nanoparticles were characterized by FT-IR, EDX analysis and TEM studies and their luminescence and scintillation responses under UV and X-ray excitation were studied and compared with that of CeF3 single crystal. © 2013 The Royal Society of Chemistry.


Azizi S.,University of Monastir | Braik M.,University of Monastir | Dridi C.,University of Monastir | Dridi C.,University of Sousse | And 3 more authors.
Applied Physics A: Materials Science and Processing | Year: 2012

Hybrid devices based on silicon nanowires (SiNWs) dispersed in a conjugated polymer poly(3-hexylthiophene) P3HT thin films have been realized. The carrier transportmechanism in inorganic/organic hybrid nancocomposites consisting of SiNW dispersed in P3HT layer was investigated by using I-V characteristics and impedance spectroscopy measurements. The conduction mechanism in these hybrid nanocomposites has been identified to be thermionic emission at the interfaces. The electrical parameters of the structure have been investigated by modelization of the I-V characteristics using an electrical equivalent circuit and have been extracted for the different SiNW volume ratios. The barrier height, the series resistance and the shunt resistance values of the diodes have been calculated as about 0.9 eV, several kω and several Mω, respectively. The diode behaves as non-ideal one because of the series resistance and the Donor/Acceptor interface layer. The impedance spectroscopy study, in the frequency range 100 Hz-100 kHz, shows a typical behavior of disordered materials and indicative of a hopping transport in the investigated temperature range. The dc conductivity follows the Arrhenius law with an activation energy transition from 8.4 to 55.8 meV at about 294 K. © Springer-Verlag 2012.


Alalga A.,Annaba University | Benabdeslem K.,University of Lyon1 | Taleb N.,Annaba University
Knowledge and Information Systems | Year: 2015

Feature selection, semi-supervised learning and multi-label classification are different challenges for machine learning and data mining communities. While other works have addressed each of these problems separately, in this paper we show how they can be addressed together. We propose a unified framework for semi-supervised multi-label feature selection, based on Laplacian score. In particular, we show how to constrain the function of this score, when data are partially labeled and each instance is associated with a set of labels. We transform the labeled part of data into soft constraints and show how to integrate them in a measure of feature relevance, according to the available labels. Experiments on benchmark data sets are provided for validating the proposed approach and comparing it with some other state-of-the-art feature selection methods in a multi-label context. © 2015 Springer-Verlag London


Benabdeslem K.,University of Lyon1 | Hindawi M.,Zirve University | Makkhongkaew R.,University of Lyon1
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Semi-supervised feature selection has become more important as the number of features has increased in partially labeled data sets. In this paper we present a feature weighting-based model to address this problem. Our proposal is based on a semi-supervised clustering paradigm that can rank features according to their relevance from high-dimensional data. We propose an adaptation of the constrained KMeans algorithm to semi-supervised feature selection by an embedded approach. Experiments are provided on several known data sets for validating our proposal. The results are promising and competitive with several representative methods. © Springer International Publishing Switzerland 2015.


Benabdeslem K.,University of Lyon1 | Elghazel H.,University of Lyon1 | Hindawi M.,Zirve University
Knowledge and Information Systems | Year: 2015

In this paper, we propose an efficient and robust approach for semi-supervised feature selection, based on the constrained Laplacian score. The main drawback of this method is the choice of the scant supervision information, represented by pairwise constraints. In fact, constraints are proven to have some noise which may deteriorate learning performance. In this work, we try to override any negative effects of constraint set by the variation of their sources. This is achieved by an ensemble technique using both a resampling of data (bagging) and a random subspace strategy. Experiments on high-dimensional datasets are provided for validating the proposed approach and comparing it with other representative feature selection methods. © 2015 Springer-Verlag London


Allab K.,University of Lyon1 | Benabdeslem K.,University of Lyon1
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

In this paper, we propose to adapt the batch version of self-organizing map (SOM) to background information in clustering task. It deals with constrained clustering with SOM in a deterministic paradigm. In this context we adapt the appropriate topological clustering to pairwise instance level constraints with the study of their informativeness and coherence properties for measuring their utility for the semi-supervised learning process. These measures will provide guidance in selecting the most useful constraint sets for the proposed algorithm. Experiments will be given over several databases for validating our approach in comparison with another constrained clustering ones. © 2011 Springer-Verlag.


Benabdeslem K.,University of Lyon1 | Hindawi M.,University of Lyon1
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

In this paper, we address the problem of semi-supervised feature selection from high-dimensional data. It aims to select the most discriminative and informative features for data analysis. This is a recent addressed challenge in feature selection research when dealing with small labeled data sampled with large unlabeled data in the same set. We present a filter based approach by constraining the known Laplacian score. We evaluate the relevance of a feature according to its locality preserving and constraints preserving ability. The problem is then presented in the spectral graph theory framework with a study of the complexity of the proposed algorithm. Finally, experimental results will be provided for validating our proposal in comparison with other known feature selection methods. © 2011 Springer-Verlag.

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