4,920 research outputs found
Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory
Land cover classification using multispectral satellite image is a very
challenging task with numerous practical applications. We propose a multi-stage
classifier that involves fuzzy rule extraction from the training data and then
generation of a possibilistic label vector for each pixel using the fuzzy rule
base. To exploit the spatial correlation of land cover types we propose four
different information aggregation methods which use the possibilistic class
label of a pixel and those of its eight spatial neighbors for making the final
classification decision. Three of the aggregation methods use Dempster-Shafer
theory of evidence while the remaining one is modeled after the fuzzy k-NN
rule. The proposed methods are tested with two benchmark seven channel
satellite images and the results are found to be quite satisfactory. They are
also compared with a Markov random field (MRF) model-based contextual
classification method and found to perform consistently better.Comment: 14 pages, 2 figure
A Labelling Framework for Probabilistic Argumentation
The combination of argumentation and probability paves the way to new
accounts of qualitative and quantitative uncertainty, thereby offering new
theoretical and applicative opportunities. Due to a variety of interests,
probabilistic argumentation is approached in the literature with different
frameworks, pertaining to structured and abstract argumentation, and with
respect to diverse types of uncertainty, in particular the uncertainty on the
credibility of the premises, the uncertainty about which arguments to consider,
and the uncertainty on the acceptance status of arguments or statements.
Towards a general framework for probabilistic argumentation, we investigate a
labelling-oriented framework encompassing a basic setting for rule-based
argumentation and its (semi-) abstract account, along with diverse types of
uncertainty. Our framework provides a systematic treatment of various kinds of
uncertainty and of their relationships and allows us to back or question
assertions from the literature
Designing fuzzy rule based classifier using self-organizing feature map for analysis of multispectral satellite images
We propose a novel scheme for designing fuzzy rule based classifier. An SOFM
based method is used for generating a set of prototypes which is used to
generate a set of fuzzy rules. Each rule represents a region in the feature
space that we call the context of the rule. The rules are tuned with respect to
their context. We justified that the reasoning scheme may be different in
different context leading to context sensitive inferencing. To realize context
sensitive inferencing we used a softmin operator with a tunable parameter. The
proposed scheme is tested on several multispectral satellite image data sets
and the performance is found to be much better than the results reported in the
literature.Comment: 23 pages, 7 figure
Uncertainty Estimation by Fisher Information-based Evidential Deep Learning
Uncertainty estimation is a key factor that makes deep learning reliable in
practical applications. Recently proposed evidential neural networks explicitly
account for different uncertainties by treating the network's outputs as
evidence to parameterize the Dirichlet distribution, and achieve impressive
performance in uncertainty estimation. However, for high data uncertainty
samples but annotated with the one-hot label, the evidence-learning process for
those mislabeled classes is over-penalized and remains hindered. To address
this problem, we propose a novel method, Fisher Information-based Evidential
Deep Learning (-EDL). In particular, we introduce Fisher
Information Matrix (FIM) to measure the informativeness of evidence carried by
each sample, according to which we can dynamically reweight the objective loss
terms to make the network more focused on the representation learning of
uncertain classes. The generalization ability of our network is further
improved by optimizing the PAC-Bayesian bound. As demonstrated empirically, our
proposed method consistently outperforms traditional EDL-related algorithms in
multiple uncertainty estimation tasks, especially in the more challenging
few-shot classification settings
Rank-Aware Negative Training for Semi-Supervised Text Classification
Semi-supervised text classification-based paradigms (SSTC) typically employ
the spirit of self-training. The key idea is to train a deep classifier on
limited labeled texts and then iteratively predict the unlabeled texts as their
pseudo-labels for further training. However, the performance is largely
affected by the accuracy of pseudo-labels, which may not be significant in
real-world scenarios. This paper presents a Rank-aware Negative Training (RNT)
framework to address SSTC in learning with noisy label manner. To alleviate the
noisy information, we adapt a reasoning with uncertainty-based approach to rank
the unlabeled texts based on the evidential support received from the labeled
texts. Moreover, we propose the use of negative training to train RNT based on
the concept that ``the input instance does not belong to the complementary
label''. A complementary label is randomly selected from all labels except the
label on-target. Intuitively, the probability of a true label serving as a
complementary label is low and thus provides less noisy information during the
training, resulting in better performance on the test data. Finally, we
evaluate the proposed solution on various text classification benchmark
datasets. Our extensive experiments show that it consistently overcomes the
state-of-the-art alternatives in most scenarios and achieves competitive
performance in the others. The code of RNT is publicly available
at:https://github.com/amurtadha/RNT.Comment: TACL 202
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