Now showing items 1-10 of 186
Ensemble learning in recommender systems: combining multiple user interactions for ranking personalization
(Universidade Federal da Paraíba - UFPBNúcleo de Pesquisa e Extensão em Aplicações de Vídeo Digital - LAViDSociedade Brasileira de Computação - SBCJoão Pessoa, 2014-11)
In this paper, we propose a technique that uses multimodal interactions of users to generate a more accurate list of recommendations optimized for the user . Our approach is a response to the actual scenario on the Web ...
Data perturbation for outlier detection ensembles
(Association for Computing Machinery - ACMAalborg UniversityAalborg, 2014-06)
Outlier detection and ensemble learning are well established research directions in data mining yet the application of ensemble techniques to outlier detection has been rarely studied. Building an ensemble requires learning ...
Learning Ensembles of Neural Networks by Means of a Bayesian Artificial Immune System
(Ieee-inst Electrical Electronics Engineers IncPiscatawayEUA, 2011)
Fast adaptive stacking of ensembles
(Association for Computing Machinery - ACMUniversity of PisaScuola Superiore Sant’AnnaPisa, 2016-04)
This paper presents a new ensemble method for learning from non-stationary data streams. In these situations, massive data are constantly generated at high speed and their target function can change over time. The proposed ...
Balanced training of a hybrid ensemble method for imbalanced datasets: a case of emergency department readmission prediction
Dealing with imbalanced datasets is a recurrent issue in health-care data processing. Most literature deals with small academic datasets, so that results often do not extrapolate to the large real-life datasets, or have ...
An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning.
(Association for Computing Machinery - ACMNew York, 2014-08)
Unsupervised models can provide supplementary soft constraints to help classify new “target” data because similar instances in the target set are more likely to share the same class label. Such models can also help detect ...
An ensemble framework for identifying essential proteins
Background: Many centrality measures have been proposed to mine and characterize the correlations between network topological properties and protein essentiality. However, most of them show limited prediction accuracy, and ...
Stock closing price forecasting using ensembles of constructive neural networks
Efficient automatic systems which continuously learn over long periods of time, and manage to evolve its knowledge, by discarding obsolete parts of it and acquiring new ones to reflect recent data, are difficult to be ...
ENSEMBLE LEARNING WITH LOCAL DIVERSITY
Integrated application of enhanced replacement method and ensemble learning for the prediction of BCRP/ABCG2 substrates
(Bentham Science Publishers, 2017-06)
Background: Breast Cancer Resistance Protein (BCRP or ABCG2) is a polyspecific efflux transporter which belongs to the ATP-binding Cassette superfamily. Up-regulation of BCRP is associated to multi-drug resistance in a ...