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Found 13 papers, 3 papers with code
$F_β$-plot -- a visual tool for evaluating imbalanced data classifiers
Although it is pretty common to have metrics indicating the classification quality within each class, for the end user, the analysis of several such metrics is then required, which in practice causes difficulty in interpreting the usefulness of a given classifier.
Continual Learning with Weight Interpolation
Continual learning poses a fundamental challenge for modern machine learning systems, requiring models to adapt to new tasks while retaining knowledge from previous ones.
A Natural Gas Consumption Forecasting System for Continual Learning Scenarios based on Hoeffding Trees with Change Point Detection Mechanism
Forecasting natural gas consumption, considering seasonality and trends, is crucial in planning its supply and consumption and optimizing the cost of obtaining it, mainly by industrial entities.
Transfer of knowledge among instruments in automatic music transcription
Achieved results prove that using synthesized data for training may be a good base for pretraining general-purpose models, where the task of transcription is not focused on one instrument.
Scalable handwritten text recognition system for lexicographic sources of under-resourced languages and alphabets
Our study provides a working solution that reads the cards, and links their lemmas to a searchable list of dictionary entries, for a large historical dictionary entitled the Dictionary of the 17th- and 18th-century Polish, which comprizes 2. 8 million index cards.
Combining Self-labeling with Selective Sampling
We propose a new method that builds an ensemble classifier.
Lifelong Learning Natural Language Processing Approach for Multilingual Data Classification
The abundance of information in digital media, which in today's world is the main source of knowledge about current events for the masses, makes it possible to spread disinformation on a larger scale than ever before.
Increasing Depth of Neural Networks for Life-long Learning
Purpose: We propose a novel method for continual learning based on the increasing depth of neural networks.
Employing chunk size adaptation to overcome concept drift
Additionally, the possibility of concept drift appearance causes that the used algorithms must be ready for the continuous adaptation of the model to the changing data distributions.
RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification
Our $5\times2$ cross-validated results on 57 benchmark binary datasets with 9 classifiers show that RB-CCR achieves a better precision-recall trade-off than CCR and generally out-performs the state-of-the-art resampling methods in terms of AUC and G-mean.
Hellinger Distance Weighted Ensemble for Imbalanced Data Stream Classification
The paper includes an experimental evaluation of the method based on the conducted experiments.
Combined Cleaning and Resampling Algorithm for Multi-Class Imbalanced Data with Label Noise
The proposed method utilizes an energy-based approach to modeling the regions suitable for oversampling, less affected by small disjuncts and outliers than SMOTE.
Monotonic classification: an overview on algorithms, performance measures and data sets
Currently, knowledge discovery in databases is an essential step to identify valid, novel and useful patterns for decision making.
