5,739 research outputs found
Survey of data mining approaches to user modeling for adaptive hypermedia
The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the applicatio
Classification of Time-Series Images Using Deep Convolutional Neural Networks
Convolutional Neural Networks (CNN) has achieved a great success in image
recognition task by automatically learning a hierarchical feature
representation from raw data. While the majority of Time-Series Classification
(TSC) literature is focused on 1D signals, this paper uses Recurrence Plots
(RP) to transform time-series into 2D texture images and then take advantage of
the deep CNN classifier. Image representation of time-series introduces
different feature types that are not available for 1D signals, and therefore
TSC can be treated as texture image recognition task. CNN model also allows
learning different levels of representations together with a classifier,
jointly and automatically. Therefore, using RP and CNN in a unified framework
is expected to boost the recognition rate of TSC. Experimental results on the
UCR time-series classification archive demonstrate competitive accuracy of the
proposed approach, compared not only to the existing deep architectures, but
also to the state-of-the art TSC algorithms.Comment: The 10th International Conference on Machine Vision (ICMV 2017
Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods
Background The prediction of human gene–abnormal phenotype associations is a
fundamental step toward the discovery of novel genes associated with human
disorders, especially when no genes are known to be associated with a specific
disease. In this context the Human Phenotype Ontology (HPO) provides a
standard categorization of the abnormalities associated with human diseases.
While the problem of the prediction of gene–disease associations has been
widely investigated, the related problem of gene–phenotypic feature (i.e., HPO
term) associations has been largely overlooked, even if for most human genes
no HPO term associations are known and despite the increasing application of
the HPO to relevant medical problems. Moreover most of the methods proposed in
literature are not able to capture the hierarchical relationships between HPO
terms, thus resulting in inconsistent and relatively inaccurate predictions.
Results We present two hierarchical ensemble methods that we formally prove to
provide biologically consistent predictions according to the hierarchical
structure of the HPO. The modular structure of the proposed methods, that
consists in a “flat” learning first step and a hierarchical combination of the
predictions in the second step, allows the predictions of virtually any flat
learning method to be enhanced. The experimental results show that
hierarchical ensemble methods are able to predict novel associations between
genes and abnormal phenotypes with results that are competitive with state-of-
the-art algorithms and with a significant reduction of the computational
complexity. Conclusions Hierarchical ensembles are efficient computational
methods that guarantee biologically meaningful predictions that obey the true
path rule, and can be used as a tool to improve and make consistent the HPO
terms predictions starting from virtually any flat learning method. The
implementation of the proposed methods is available as an R package from the
CRAN repository
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