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Found 16 papers, 10 papers with code
Probabilistic Truly Unordered Rule Sets
To address these shortcomings, we propose TURS, for Truly Unordered Rule Sets.
Graph Neural Networks based Log Anomaly Detection and Explanation
Event logs are widely used to record the status of high-tech systems, making log anomaly detection important for monitoring those systems.
Explainable Contextual Anomaly Detection using Quantile Regression Forests
Traditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally.
A Survey on Explainable Anomaly Detection
In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation of outcomes to practitioners.
Feature Selection for Fault Detection and Prediction based on Event Log Analysis
Event logs are widely used for anomaly detection and prediction in complex systems.
Truly Unordered Probabilistic Rule Sets for Multi-class Classification
Still, existing methods have several shortcomings: 1) most recent methods require a binary feature matrix as input, while learning rules directly from numeric variables is understudied; 2) existing methods impose orders among rules, either explicitly or implicitly, which harms interpretability; and 3) currently no method exists for learning probabilistic rule sets for multi-class target variables (there is only one for probabilistic rule lists).
Robust subgroup discovery
This novel model class allows us to formalise the problem of optimal robust subgroup discovery using the Minimum Description Length (MDL) principle, where we resort to optimal Normalised Maximum Likelihood and Bayesian encodings for nominal and numeric targets, respectively.
Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms
Further, we show that CMI can be consistently estimated for discrete-continuous mixture variables by learning an adaptive histogram model.
Causal Discovery
Information Theory
Information Theory
Applications
Discovering outstanding subgroup lists for numeric targets using MDL
We propose a dispersion-aware problem formulation for subgroup set discovery that is based on the minimum description length (MDL) principle and subgroup lists.
Unsupervised Discretization by Two-dimensional MDL-based Histogram
As the flexibility of our model class comes at the cost of a vast search space, we introduce a heuristic algorithm, named PALM, which Partitions each dimension ALternately and then Merges neighboring regions, all using the MDL principle.
Vouw: Geometric Pattern Mining using the MDL Principle
We introduce geometric pattern mining, the problem of finding recurring local structure in discrete, geometric matrices.
Interpretable multiclass classification by MDL-based rule lists
Interpretable classifiers have recently witnessed an increase in attention from the data mining community because they are inherently easier to understand and explain than their more complex counterparts.
Learning what matters - Sampling interesting patterns
We compare the performance of the proposed algorithm to the state-of-the-art in interactive pattern mining by emulating the interests of a user.
Local Subspace-Based Outlier Detection using Global Neighbourhoods
In highly complex and high-dimensional data, however, existing methods are likely to overlook important outliers because they do not explicitly take into account that the data is often a mixture distribution of multiple components.
Flexible constrained sampling with guarantees for pattern mining
Several sampling algorithms have been proposed, but each of them has its limitations when it comes to 1) flexibility in terms of quality measures and constraints that can be used, and/or 2) guarantees with respect to sampling accuracy.
Evolving the Structure of Evolution Strategies
Various variants of the well known Covariance Matrix Adaptation Evolution Strategy (CMA-ES) have been proposed recently, which improve the empirical performance of the original algorithm by structural modifications.
