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Found 20 papers, 6 papers with code
A Neuro-Symbolic Explainer for Rare Events: A Case Study on Predictive Maintenance
The system can present global explanations for the black box model and local explanations for why the black box model predicts a failure.
Super-Resolution Analysis for Landfill Waste Classification
Illegal landfills are a critical issue due to their environmental, economic, and public health impacts.
S+t-SNE - Bringing dimensionality reduction to data streams
We present S+t-SNE, an adaptation of the t-SNE algorithm designed to handle infinite data streams.
Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning
In the evolving field of machine learning, ensuring fairness has become a critical concern, prompting the development of algorithms designed to mitigate discriminatory outcomes in decision-making processes.
Explainable Predictive Maintenance
We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation
The suite was generated by applying state-of-the-art tabular data generation techniques on an anonymized, real-world bank account opening fraud detection dataset.
A Benchmark dataset for predictive maintenance
The paper describes the MetroPT data set, an outcome of a eXplainable Predictive Maintenance (XPM) project with an urban metro public transportation service in Porto, Portugal.
Contextualization for the Organization of Text Documents Streams
The results provide a new view for the contextualization of similarity when approaching flux of documents organization tasks, based on the similarity between documents in the flux, and by using mentioned algorithms.
Federated Anomaly Detection over Distributed Data Streams
This work complements the state-of-the-art by adapting the data stream algorithms in a federated learning setting for anomaly detection and by delivering a robust framework and demonstrating the practical feasibility in a real-world distributed deployment scenario.
Forecasting Financial Market Structure from Network Features using Machine Learning
For such, market structure is modeled as a dynamic asset network by quantifying time-dependent co-movement of asset price returns across company constituents of major global market indices.
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations
We conducted an experiment following XAI Test to evaluate three popular post-hoc explanation methods -- LIME, SHAP, and TreeInterpreter -- on a real-world fraud detection task, with real data, a deployed ML model, and fraud analysts.
Decision Making
Explainable Artificial Intelligence (XAI)
+1
Mining Human Mobility Data to Discover Locations and Habits
Many aspects of life are associated with places of human mobility patterns and nowadays we are facing an increase in the pervasiveness of mobile devices these individuals carry.
Contextual One-Class Classification in Data Streams
We conclude that the paradigm of contexts in data streams can be used to improve the performance of streaming one-class classifiers.
A scalable saliency-based Feature selection method with instance level information
Classic feature selection techniques remove those features that are either irrelevant or redundant, achieving a subset of relevant features that help to provide a better knowledge extraction.
Identifying Points of Interest and Similar Individuals from Raw GPS Data
Smartphones and portable devices have become ubiquitous and part of everyone's life.
EigenEvent: An Algorithm for Event Detection from Complex Data Streams in Syndromic Surveillance
Experimental results on hundred sets of benchmark data reveals that EigenEvent presents a better overall performance comparing state-of-the-art, in particular in terms of the false alarm rate.
Eigenspace Method for Spatiotemporal Hotspot Detection
STScan makes some restrictive assumptions about the distribution of data, the shape of the hotspots and the quality of data, which can be unrealistic for some nontraditional data sources.
Event and Anomaly Detection Using Tucker3 Decomposition
Among unsupervised approaches, Principal Component Analysis (PCA) is a well-known solution which has been widely used in the anomaly detection literature and can be applied to matrix data (e. g. Users-Features).
Posterior vs Parameter Sparsity in Latent Variable Models
We apply this new method to learn first-order HMMs for unsupervised part-of-speech (POS) tagging, and show that HMMs learned this way consistently and significantly out-performs both EM-trained HMMs, and HMMs with a sparsity-inducing Dirichlet prior trained by variational EM.
Learning with local drift detection
In this work we present experiments using the method as a wrapper over a decision tree and a linear model, and in each internal-node of a decision tree.
