264 research outputs found
Multilabel classification by BCH code and random forests
This paper uses error correcting codes for multilabel classification. BCH code and random forests learner are used to form the proposed method. Thus, the advantage of the error-correcting properties of BCH is merged with the good performance of the random forests learner to enhance the multilabel classification results. Three experiments are conducted on three common benchmark datasets. The results are compared against those of several exiting approaches. The proposed method does well against its counterparts for the three datasets of varying characteristics.<br /
Semantic Interleaving Global Channel Attention for Multilabel Remote Sensing Image Classification
Multi-Label Remote Sensing Image Classification (MLRSIC) has received
increasing research interest. Taking the cooccurrence relationship of multiple
labels as additional information helps to improve the performance of this task.
Current methods focus on using it to constrain the final feature output of a
Convolutional Neural Network (CNN). On the one hand, these methods do not make
full use of label correlation to form feature representation. On the other
hand, they increase the label noise sensitivity of the system, resulting in
poor robustness. In this paper, a novel method called Semantic Interleaving
Global Channel Attention (SIGNA) is proposed for MLRSIC. First, the label
co-occurrence graph is obtained according to the statistical information of the
data set. The label co-occurrence graph is used as the input of the Graph
Neural Network (GNN) to generate optimal feature representations. Then, the
semantic features and visual features are interleaved, to guide the feature
expression of the image from the original feature space to the semantic feature
space with embedded label relations. SIGNA triggers global attention of feature
maps channels in a new semantic feature space to extract more important visual
features. Multihead SIGNA based feature adaptive weighting networks are
proposed to act on any layer of CNN in a plug-and-play manner. For remote
sensing images, better classification performance can be achieved by inserting
CNN into the shallow layer. We conduct extensive experimental comparisons on
three data sets: UCM data set, AID data set, and DFC15 data set. Experimental
results demonstrate that the proposed SIGNA achieves superior classification
performance compared to state-of-the-art (SOTA) methods. It is worth mentioning
that the codes of this paper will be open to the community for reproducibility
research. Our codes are available at https://github.com/kyle-one/SIGNA.Comment: 14 pages, 13 figure
Rectifying classifier chains for multi-label classification
Classifier chains have recently been proposed as an appealing method for tackling the multi-label classification task. In addition to several empirical studies showing its state-of-the-art performance, especially when being used in its ensemble variant, there are also some first results on theoretical properties of classifier chains. Continuing along this line, we analyze the influence of a potential pitfall of the learning process, namely the discrepancy between the feature spaces used in training and testing: While true class labels are used as supplementary attributes for training the binary models along the chain, the same models need to rely on estimations of these labels at prediction time. We elucidate under which circumstances the attribute noise thus created can affect the overall prediction performance. As a result of our findings, we propose two modifications of classifier chains that are meant to overcome this problem. Experimentally, we show that our variants are indeed able to produce better results in cases where the original chaining process is likely to fai
Noisy multi-label semi-supervised dimensionality reduction
Noisy labeled data represent a rich source of information that often are
easily accessible and cheap to obtain, but label noise might also have many
negative consequences if not accounted for. How to fully utilize noisy labels
has been studied extensively within the framework of standard supervised
machine learning over a period of several decades. However, very little
research has been conducted on solving the challenge posed by noisy labels in
non-standard settings. This includes situations where only a fraction of the
samples are labeled (semi-supervised) and each high-dimensional sample is
associated with multiple labels. In this work, we present a novel
semi-supervised and multi-label dimensionality reduction method that
effectively utilizes information from both noisy multi-labels and unlabeled
data. With the proposed Noisy multi-label semi-supervised dimensionality
reduction (NMLSDR) method, the noisy multi-labels are denoised and unlabeled
data are labeled simultaneously via a specially designed label propagation
algorithm. NMLSDR then learns a projection matrix for reducing the
dimensionality by maximizing the dependence between the enlarged and denoised
multi-label space and the features in the projected space. Extensive
experiments on synthetic data, benchmark datasets, as well as a real-world case
study, demonstrate the effectiveness of the proposed algorithm and show that it
outperforms state-of-the-art multi-label feature extraction algorithms.Comment: 38 page
fMRI Pattern Classification using Neuroanatomically Constrained Boosting
Pattern classification in functional MRI (fMRI) is a novel methodology to automatically identify differences in distributed neural substrates resulting from cognitive tasks. Reliable pattern classification is challenging due to the high dimensionality of fMRI data, the small number of available data sets, interindividual differences, and dependence on the acquisition methodology. Thus, most previous fMRI classification methods were applied in individual subjects. In this study, we developed a novel approach to improve multiclass classification across groups of subjects, field strengths, and fMRI methods. Spatially normalized activation maps were segmented into functional areas using a neuroanatomical atlas and each map was classified separately using local classifiers. A single multiclass output was applied using a weighted aggregation of the classifier’s outputs. An Adaboost technique was applied, modified to find the optimal aggregation of a set of spatially distributed classifiers. This Adaboost combined the regionspecific classifiers to achieve improved classification accuracy with respect to conventional techniques. Multiclass classification accuracy was assessed in an fMRI group study with interleaved motor, visual, auditory, and cognitive task design. Data were acquired across 18 subjects at different field strengths (1.5 T, 4 T), with different pulse sequence parameters (voxel size and readout bandwidth). Misclassification rates of the boosted classifier were between 3.5% and 10%, whereas for the single classifier, these were between 15% and 23%, suggesting that the boosted classifier provides a better generalization ability together with better robustness. The high computational speed of boosting classification makes it attractive for real-time fMRI to facilitate online interpretation of dynamically changing activation patternsPublicad
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