2,658 research outputs found
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
One of the challenges in modeling cognitive events from electroencephalogram
(EEG) data is finding representations that are invariant to inter- and
intra-subject differences, as well as to inherent noise associated with such
data. Herein, we propose a novel approach for learning such representations
from multi-channel EEG time-series, and demonstrate its advantages in the
context of mental load classification task. First, we transform EEG activities
into a sequence of topology-preserving multi-spectral images, as opposed to
standard EEG analysis techniques that ignore such spatial information. Next, we
train a deep recurrent-convolutional network inspired by state-of-the-art video
classification to learn robust representations from the sequence of images. The
proposed approach is designed to preserve the spatial, spectral, and temporal
structure of EEG which leads to finding features that are less sensitive to
variations and distortions within each dimension. Empirical evaluation on the
cognitive load classification task demonstrated significant improvements in
classification accuracy over current state-of-the-art approaches in this field.Comment: To be published as a conference paper at ICLR 201
Web Page Multiclass Classification
As the internet age evolves, the volume of content hosted on the Web is rapidly expanding. With this ever-expanding content, the capability to accurately categorize web pages is a current challenge to serve many use cases. This paper proposes a variation in the approach to text preprocessing pipeline whereby noun phrase extraction is performed first followed by lemmatization, contraction expansion, removing special characters, removing extra white space, lower casing, and removal of stop words. The first step of noun phrase extraction is aimed at reducing the set of terms to those that best describe what the web pages are about to improve the categorization capabilities of the model. Separately, a text preprocessing using keyword extraction is evaluated. In addition to the text preprocessing techniques mentioned, feature reduction techniques are applied to optimize model performance. Several modeling techniques are examined using these two approaches and are compared to a baseline model. The baseline model is a Support Vector Machine with linear kernel and is based on text preprocessing and feature reduction techniques that do not include noun phrase extraction or keyword extraction and uses stemming rather than lemmatization. The recommended SVM One-Versus-One model based on noun phrase extraction and lemmatization during text preprocessing shows an accuracy improvement over the baseline model of nearly 1% and a 5-fold reduction in misclassification of web pages as undesirable categories
Automatic figure classification in bioscience literature
AbstractMillions of figures appear in biomedical articles, and it is important to develop an intelligent figure search engine to return relevant figures based on user entries. In this study we report a figure classifier that automatically classifies biomedical figures into five predefined figure types: Gel-image, Image-of-thing, Graph, Model, and Mix. The classifier explored rich image features and integrated them with text features. We performed feature selection and explored different classification models, including a rule-based figure classifier, a supervised machine-learning classifier, and a multi-model classifier, the latter of which integrated the first two classifiers. Our results show that feature selection improved figure classification and the novel image features we explored were the best among image features that we have examined. Our results also show that integrating text and image features achieved better performance than using either of them individually. The best system is a multi-model classifier which combines the rule-based hierarchical classifier and a support vector machine (SVM) based classifier, achieving a 76.7% F1-score for five-type classification. We demonstrated our system at http://figureclassification.askhermes.org/
A Survey on Graph Kernels
Graph kernels have become an established and widely-used technique for
solving classification tasks on graphs. This survey gives a comprehensive
overview of techniques for kernel-based graph classification developed in the
past 15 years. We describe and categorize graph kernels based on properties
inherent to their design, such as the nature of their extracted graph features,
their method of computation and their applicability to problems in practice. In
an extensive experimental evaluation, we study the classification accuracy of a
large suite of graph kernels on established benchmarks as well as new datasets.
We compare the performance of popular kernels with several baseline methods and
study the effect of applying a Gaussian RBF kernel to the metric induced by a
graph kernel. In doing so, we find that simple baselines become competitive
after this transformation on some datasets. Moreover, we study the extent to
which existing graph kernels agree in their predictions (and prediction errors)
and obtain a data-driven categorization of kernels as result. Finally, based on
our experimental results, we derive a practitioner's guide to kernel-based
graph classification
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