Balancing Efficiency vs. Effectiveness and Providing Missing Label
Robustness in Multi-Label Stream Classification
- URL: http://arxiv.org/abs/2310.00665v1
- Date: Sun, 1 Oct 2023 13:23:37 GMT
- Title: Balancing Efficiency vs. Effectiveness and Providing Missing Label
Robustness in Multi-Label Stream Classification
- Authors: Sepehr Bakhshi and Fazli Can
- Abstract summary: We propose a neural network-based approach to high-dimensional multi-label classification.
Our model uses a selective concept drift adaptation mechanism that makes it suitable for a non-stationary environment.
We adapt our model to an environment with missing labels using a simple yet effective imputation strategy.
- Score: 3.97048491084787
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Available works addressing multi-label classification in a data stream
environment focus on proposing accurate models; however, these models often
exhibit inefficiency and cannot balance effectiveness and efficiency. In this
work, we propose a neural network-based approach that tackles this issue and is
suitable for high-dimensional multi-label classification. Our model uses a
selective concept drift adaptation mechanism that makes it suitable for a
non-stationary environment. Additionally, we adapt our model to an environment
with missing labels using a simple yet effective imputation strategy and
demonstrate that it outperforms a vast majority of the state-of-the-art
supervised models. To achieve our purposes, we introduce a weighted binary
relevance-based approach named ML-BELS using the Broad Ensemble Learning System
(BELS) as its base classifier. Instead of a chain of stacked classifiers, our
model employs independent weighted ensembles, with the weights generated by the
predictions of a BELS classifier. We show that using the weighting strategy on
datasets with low label cardinality negatively impacts the accuracy of the
model; with this in mind, we use the label cardinality as a trigger for
applying the weights. We present an extensive assessment of our model using 11
state-of-the-art baselines, five synthetics, and 13 real-world datasets, all
with different characteristics. Our results demonstrate that the proposed
approach ML-BELS is successful in balancing effectiveness and efficiency, and
is robust to missing labels and concept drift.
Related papers
- An Embedding is Worth a Thousand Noisy Labels [0.11999555634662634]
We propose WANN, a weighted Adaptive Nearest Neighbor approach to address label noise.
We show WANN outperforms reference methods on diverse datasets of varying size and under various noise types and severities.
Our approach, emphasizing efficiency and explainability, emerges as a simple, robust solution to overcome the inherent limitations of deep neural network training.
arXiv Detail & Related papers (2024-08-26T15:32:31Z) - Exploring Beyond Logits: Hierarchical Dynamic Labeling Based on Embeddings for Semi-Supervised Classification [49.09505771145326]
We propose a Hierarchical Dynamic Labeling (HDL) algorithm that does not depend on model predictions and utilizes image embeddings to generate sample labels.
Our approach has the potential to change the paradigm of pseudo-label generation in semi-supervised learning.
arXiv Detail & Related papers (2024-04-26T06:00:27Z) - Graph Embedded Intuitionistic Fuzzy Random Vector Functional Link Neural
Network for Class Imbalance Learning [4.069144210024564]
We propose a graph embedded intuitionistic fuzzy RVFL for class imbalance learning (GE-IFRVFL-CIL) model incorporating a weighting mechanism to handle imbalanced datasets.
The proposed GE-IFRVFL-CIL model offers a promising solution to address the class imbalance issue, mitigates the detrimental effect of noise and outliers, and preserves the inherent geometrical structures of the dataset.
arXiv Detail & Related papers (2023-07-15T20:45:45Z) - Leveraging Instance Features for Label Aggregation in Programmatic Weak
Supervision [75.1860418333995]
Programmatic Weak Supervision (PWS) has emerged as a widespread paradigm to synthesize training labels efficiently.
The core component of PWS is the label model, which infers true labels by aggregating the outputs of multiple noisy supervision sources as labeling functions.
Existing statistical label models typically rely only on the outputs of LF, ignoring the instance features when modeling the underlying generative process.
arXiv Detail & Related papers (2022-10-06T07:28:53Z) - L2B: Learning to Bootstrap Robust Models for Combating Label Noise [52.02335367411447]
This paper introduces a simple and effective method, named Learning to Bootstrap (L2B)
It enables models to bootstrap themselves using their own predictions without being adversely affected by erroneous pseudo-labels.
It achieves this by dynamically adjusting the importance weight between real observed and generated labels, as well as between different samples through meta-learning.
arXiv Detail & Related papers (2022-02-09T05:57:08Z) - Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition [98.25592165484737]
We propose a more effective pseudo-labeling scheme, called Cross-Model Pseudo-Labeling (CMPL)
CMPL achieves $17.6%$ and $25.1%$ Top-1 accuracy on Kinetics-400 and UCF-101 using only the RGB modality and $1%$ labeled data, respectively.
arXiv Detail & Related papers (2021-12-17T18:59:41Z) - Active Learning at the ImageNet Scale [43.595076693347835]
In this work, we study a combination of active learning (AL) and pretraining (SSP) on ImageNet.
We find that performance on small toy datasets is not representative of performance on ImageNet due to the class imbalanced samples selected by an active learner.
We propose Balanced Selection (BASE), a simple, scalable AL algorithm that outperforms random sampling consistently.
arXiv Detail & Related papers (2021-11-25T02:48:51Z) - Momentum Pseudo-Labeling for Semi-Supervised Speech Recognition [55.362258027878966]
We present momentum pseudo-labeling (MPL) as a simple yet effective strategy for semi-supervised speech recognition.
MPL consists of a pair of online and offline models that interact and learn from each other, inspired by the mean teacher method.
The experimental results demonstrate that MPL effectively improves over the base model and is scalable to different semi-supervised scenarios.
arXiv Detail & Related papers (2021-06-16T16:24:55Z) - Delving Deep into Label Smoothing [112.24527926373084]
Label smoothing is an effective regularization tool for deep neural networks (DNNs)
We present an Online Label Smoothing (OLS) strategy, which generates soft labels based on the statistics of the model prediction for the target category.
arXiv Detail & Related papers (2020-11-25T08:03:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.