20,455 research outputs found
Semi-supervised Predictive Clustering Trees for (Hierarchical) Multi-label Classification
Semi-supervised learning (SSL) is a common approach to learning predictive
models using not only labeled examples, but also unlabeled examples. While SSL
for the simple tasks of classification and regression has received a lot of
attention from the research community, this is not properly investigated for
complex prediction tasks with structurally dependent variables. This is the
case of multi-label classification and hierarchical multi-label classification
tasks, which may require additional information, possibly coming from the
underlying distribution in the descriptive space provided by unlabeled
examples, to better face the challenging task of predicting simultaneously
multiple class labels.
In this paper, we investigate this aspect and propose a (hierarchical)
multi-label classification method based on semi-supervised learning of
predictive clustering trees. We also extend the method towards ensemble
learning and propose a method based on the random forest approach. Extensive
experimental evaluation conducted on 23 datasets shows significant advantages
of the proposed method and its extension with respect to their supervised
counterparts. Moreover, the method preserves interpretability and reduces the
time complexity of classical tree-based models
Semi-FCMNet : semi-supervised learning for forest cover mapping from satellite imagery via ensemble self-training and perturbation
Forest cover mapping is of paramount importance for environmental monitoring, biodiversity assessment, and forest resource management. In the realm of forest cover mapping, significant advancements have been made by leveraging fully supervised semantic segmentation models. However, the process of acquiring a substantial quantity of pixel-level labelled data is prone to time-consuming and labour-intensive procedures. To address this issue, this paper proposes a novel semi-supervised-learning-based semantic segmentation framework that leverages limited labelled and numerous unlabelled data, integrating multi-level perturbations and model ensembles. Our framework incorporates a multi-level perturbation module that integrates input-level, feature-level, and model-level perturbations. This module aids in effectively emphasising salient features from remote sensing (RS) images during different training stages and facilitates the stability of model learning, thereby effectively preventing overfitting. We also propose an ensemble-voting-based label generation strategy that enhances the reliability of model-generated labels, achieving smooth label predictions for challenging boundary regions. Additionally, we designed an adaptive loss function that dynamically adjusts the focus on poorly learned categories and dynamically adapts the attention towards labels generated during both the student and teacher stages. The proposed framework was comprehensively evaluated using two satellite RS datasets, showcasing its competitive performance in semi-supervised forest-cover-mapping scenarios. Notably, the method outperforms the fully supervised approach by 1–3% across diverse partitions, as quantified by metrics including mIoU, accuracy, and mPrecision. Furthermore, it exhibits superiority over other state-of-the-art semi-supervised methods. These results indicate the practical significance of our solution in various domains, including environmental monitoring, forest management, and conservation decision-making processes
Active Collaborative Ensemble Tracking
A discriminative ensemble tracker employs multiple classifiers, each of which
casts a vote on all of the obtained samples. The votes are then aggregated in
an attempt to localize the target object. Such method relies on collective
competence and the diversity of the ensemble to approach the target/non-target
classification task from different views. However, by updating all of the
ensemble using a shared set of samples and their final labels, such diversity
is lost or reduced to the diversity provided by the underlying features or
internal classifiers' dynamics. Additionally, the classifiers do not exchange
information with each other while striving to serve the collective goal, i.e.,
better classification. In this study, we propose an active collaborative
information exchange scheme for ensemble tracking. This, not only orchestrates
different classifier towards a common goal but also provides an intelligent
update mechanism to keep the diversity of classifiers and to mitigate the
shortcomings of one with the others. The data exchange is optimized with regard
to an ensemble uncertainty utility function, and the ensemble is updated via
co-training. The evaluations demonstrate promising results realized by the
proposed algorithm for the real-world online tracking.Comment: AVSS 2017 Submissio
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