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Found 19 papers, 5 papers with code
Multisource Semisupervised Adversarial Domain Generalization Network for Cross-Scene Sea-Land Clutter Classification
MSADGN can extract domain-invariant and domain-specific features from one labeled source domain and multiple unlabeled source domains, and then generalize these features to an arbitrary unseen target domain for real-time prediction of sea\textendash land clutter.
On Forecast Stability
To fill this gap, we propose a simple linear-interpolation-based approach that is applicable to stabilise the forecasts provided by any base model vertically and horizontally.
Semi-Supervised Constrained Clustering: An In-Depth Overview, Ranked Taxonomy and Future Research Directions
To remedy this, this study presents in-detail the background of constrained clustering and provides a novel ranked taxonomy of the types of constraints that can be used in constrained clustering.
Semi-supervised Clustering with Two Types of Background Knowledge: Fusing Pairwise Constraints and Monotonicity Constraints
This study addresses the problem of performing clustering in the presence of two types of background knowledge: pairwise constraints and monotonicity constraints.
Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization
The resulting optimization problem is a bilevel problem, where the lower level determines the support vectors and the upper level the hyperparameters.
Fuzzy k-Nearest Neighbors with monotonicity constraints: Moving towards the robustness of monotonic noise
This paper proposes a new model based on Fuzzy k-Nearest Neighbors for classification with monotonic constraints, Monotonic Fuzzy k-NN (MonFkNN).
Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations
Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper.
Smart Data driven Decision Trees Ensemble Methodology for Imbalanced Big Data
Ensembles have shown to be able to successfully address imbalanced data problems.
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms, Experimental Analysis, Prospects and Challenges (with Appendices on Mathematical Background and Detailed Algorithms Explanation)
This tutorial provides a theoretical background and foundations on this topic and a comprehensive experimental analysis of the most-known algorithms.
A snapshot on nonstandard supervised learning problems: taxonomy, relationships and methods
This field is subdivided into multiple areas, among which the best known are supervised learning (e. g. classification and regression) and unsupervised learning (e. g. clustering and association rules).
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.
OCAPIS: R package for Ordinal Classification And Preprocessing In Scala
The classification and pre-processing of this type of data is attracting more and more interest in the area of machine learning, due to its presence in many common problems.
Label Noise Filtering Techniques to Improve Monotonic Classification
Changing the class labels of the data set (relabelling) is useful for this.
DPASF: A Flink Library for Streaming Data preprocessing
Data preprocessing techniques are devoted to correct or alleviate errors in data.
BELIEF: A distance-based redundancy-proof feature selection method for Big Data
Empirical tests performed on our method show its estimation ability in manifold huge sets --both in number of features and instances--, as well as its simplified runtime cost (specially, at the redundancy detection step).
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines
Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input characteristics to generate a good model.
Enabling Smart Data: Noise filtering in Big Data classification
In any knowledge discovery process the value of extracted knowledge is directly related to the quality of the data used.
On the use of convolutional neural networks for robust classification of multiple fingerprint captures
In our experiments, convolutional neural networks yielded better accuracy and penetration rate than state-of-the-art classifiers based on explicit feature extraction.
