64,215 research outputs found
Efficient Active Novel Class Detection for Data Stream Classification
International audienceOne substantial aspect of data stream classification is the possible appearance of novel unseen classes which must be identified in order to avoid confusion with existing classes. Detecting such new classes is omitted by most existing techniques and rarely addressed in the literature. We address this issue and propose an efficient method to identify novel class emergence in a multi-class data stream. The proposed method incrementally maintains a covered feature space of existing (known) classes. An incoming data point is designated as "insider" or "outsider" depending on whether it lies inside or outside the covered space area. An insider represents a possible instance of an existing class, while an outsider may be an instance of a possible novel class. The proposed method is able to iteratively select those insiders (resp. outsiders) that are more likely to be members of a novel (resp. an existing) class, and eventually distinguish the actual novel and existing classes accurately. We show how to actively query the labels of the identified novel class instances that are most uncertain. The method also allows us to balance between the rapidity of the novelty detection and its efficiency. Experiments using real world data prove the effectiveness of our approach for both the novel class detection and classification accuracy
An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams
Existing FNNs are mostly developed under a shallow network configuration
having lower generalization power than those of deep structures. This paper
proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be
automatically extracted from data streams or removed if they play limited role
during their lifespan. The structure of the network can be deepened on demand
by stacking additional layers using a drift detection method which not only
detects the covariate drift, variations of input space, but also accurately
identifies the real drift, dynamic changes of both feature space and target
space. DEVFNN is developed under the stacked generalization principle via the
feature augmentation concept where a recently developed algorithm, namely
gClass, drives the hidden layer. It is equipped by an automatic feature
selection method which controls activation and deactivation of input attributes
to induce varying subsets of input features. A deep network simplification
procedure is put forward using the concept of hidden layer merging to prevent
uncontrollable growth of dimensionality of input space due to the nature of
feature augmentation approach in building a deep network structure. DEVFNN
works in the sample-wise fashion and is compatible for data stream
applications. The efficacy of DEVFNN has been thoroughly evaluated using seven
datasets with non-stationary properties under the prequential test-then-train
protocol. It has been compared with four popular continual learning algorithms
and its shallow counterpart where DEVFNN demonstrates improvement of
classification accuracy. Moreover, it is also shown that the concept drift
detection method is an effective tool to control the depth of network structure
while the hidden layer merging scenario is capable of simplifying the network
complexity of a deep network with negligible compromise of generalization
performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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