416 research outputs found
A Robust Multilabel Method Integrating Rule-based Transparent Model, Soft Label Correlation Learning and Label Noise Resistance
Model transparency, label correlation learning and the robust-ness to label
noise are crucial for multilabel learning. However, few existing methods study
these three characteristics simultaneously. To address this challenge, we
propose the robust multilabel Takagi-Sugeno-Kang fuzzy system (R-MLTSK-FS) with
three mechanisms. First, we design a soft label learning mechanism to reduce
the effect of label noise by explicitly measuring the interactions between
labels, which is also the basis of the other two mechanisms. Second, the
rule-based TSK FS is used as the base model to efficiently model the inference
relationship be-tween features and soft labels in a more transparent way than
many existing multilabel models. Third, to further improve the performance of
multilabel learning, we build a correlation enhancement learning mechanism
based on the soft label space and the fuzzy feature space. Extensive
experiments are conducted to demonstrate the superiority of the proposed
method.Comment: This paper has been accepted by IEEE Transactions on Fuzzy System
Multilabel Prototype Generation for Data Reduction in k-Nearest Neighbour classification
Prototype Generation (PG) methods are typically considered for improving the
efficiency of the -Nearest Neighbour (NN) classifier when tackling
high-size corpora. Such approaches aim at generating a reduced version of the
corpus without decreasing the classification performance when compared to the
initial set. Despite their large application in multiclass scenarios, very few
works have addressed the proposal of PG methods for the multilabel space. In
this regard, this work presents the novel adaptation of four multiclass PG
strategies to the multilabel case. These proposals are evaluated with three
multilabel NN-based classifiers, 12 corpora comprising a varied range of
domains and corpus sizes, and different noise scenarios artificially induced in
the data. The results obtained show that the proposed adaptations are capable
of significantly improving -- both in terms of efficiency and classification
performance -- the only reference multilabel PG work in the literature as well
as the case in which no PG method is applied, also presenting a statistically
superior robustness in noisy scenarios. Moreover, these novel PG strategies
allow prioritising either the efficiency or efficacy criteria through its
configuration depending on the target scenario, hence covering a wide area in
the solution space not previously filled by other works
Multilabel Prototype Generation for data reduction in K-Nearest Neighbour classification
Prototype Generation (PG) methods are typically considered for improving the efficiency of the k-Nearest Neighbour (kNN) classifier when tackling high-size corpora. Such approaches aim at generating a reduced version of the corpus without decreasing the classification performance when compared to the initial set. Despite their large application in multiclass scenarios, very few works have addressed the proposal of PG methods for the multilabel space. In this regard, this work presents the novel adaptation of four multiclass PG strategies to the multilabel case. These proposals are evaluated with three multilabel kNN-based classifiers, 12 corpora comprising a varied range of domains and corpus sizes, and different noise scenarios artificially induced in the data. The results obtained show that the proposed adaptations are capable of significantly improving—both in terms of efficiency and classification performance—the only reference multilabel PG work in the literature as well as the case in which no PG method is applied, also presenting statistically superior robustness in noisy scenarios. Moreover, these novel PG strategies allow prioritising either the efficiency or efficacy criteria through its configuration depending on the target scenario, hence covering a wide area in the solution space not previously filled by other works.This research was partially funded by the Spanish Ministerio de Ciencia e Innovación through the MultiScore (PID2020-118447RA-I00) and DOREMI (TED2021-132103A-I00) projects. The first author is supported by grant APOSTD/2020/256 from “Programa I+D+i de la Generalitat Valenciana”
HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
In this paper have developed a novel hybrid hierarchical attention-based
bidirectional recurrent neural network with dilated CNN (HARDC) method for
arrhythmia classification. This solves problems that arise when traditional
dilated convolutional neural network (CNN) models disregard the correlation
between contexts and gradient dispersion. The proposed HARDC fully exploits the
dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM)
architecture to generate fusion features. As a result of incorporating both
local and global feature information and an attention mechanism, the model's
performance for prediction is improved.By combining the fusion features with a
dilated CNN and a hierarchical attention mechanism, the trained HARDC model
showed significantly improved classification results and interpretability of
feature extraction on the PhysioNet 2017 challenge dataset. Sequential Z-Score
normalization, filtering, denoising, and segmentation are used to prepare the
raw data for analysis. CGAN (Conditional Generative Adversarial Network) is
then used to generate synthetic signals from the processed data. The
experimental results demonstrate that the proposed HARDC model significantly
outperforms other existing models, achieving an accuracy of 99.60\%, F1 score
of 98.21\%, a precision of 97.66\%, and recall of 99.60\% using MIT-BIH
generated ECG. In addition, this approach substantially reduces run time when
using dilated CNN compared to normal convolution. Overall, this hybrid model
demonstrates an innovative and cost-effective strategy for ECG signal
compression and high-performance ECG recognition. Our results indicate that an
automated and highly computed method to classify multiple types of arrhythmia
signals holds considerable promise.Comment: 23 page
Transductive Multi-label Zero-shot Learning
Zero-shot learning has received increasing interest as a means to alleviate
the often prohibitive expense of annotating training data for large scale
recognition problems. These methods have achieved great success via learning
intermediate semantic representations in the form of attributes and more
recently, semantic word vectors. However, they have thus far been constrained
to the single-label case, in contrast to the growing popularity and importance
of more realistic multi-label data. In this paper, for the first time, we
investigate and formalise a general framework for multi-label zero-shot
learning, addressing the unique challenge therein: how to exploit multi-label
correlation at test time with no training data for those classes? In
particular, we propose (1) a multi-output deep regression model to project an
image into a semantic word space, which explicitly exploits the correlations in
the intermediate semantic layer of word vectors; (2) a novel zero-shot learning
algorithm for multi-label data that exploits the unique compositionality
property of semantic word vector representations; and (3) a transductive
learning strategy to enable the regression model learned from seen classes to
generalise well to unseen classes. Our zero-shot learning experiments on a
number of standard multi-label datasets demonstrate that our method outperforms
a variety of baselines.Comment: 12 pages, 6 figures, Accepted to BMVC 2014 (oral
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