A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
XGBoost fails to accurately identify relevant features, while interpretable methods do.
Paper introduces MCRP for estimating feature relevance uncertainty in neural networks.
GRM uses graph neural networks to score process activity relevance.
The paper investigates how irrelevant features affect clustering performance.
FUJI scores similarity of ranked lists more robustly.
New local MDI variable importances derived from global scores match Shapley values.
Gradual pruning reduces inference cost by pruning least important channels during training.
Research creates a machine learning model for predicting TAVI patient mortality.
Evaluating, explaining, and visualizing high-level concepts in generative models, such as variational autoencoders (VAEs), is challenging in part due to a lack of known prediction classes that are required to generate saliency maps in supervised learning. While saliency maps may help identify relevant features (e.g., p…
In this paper, a novel feature selection method is presented, which is based on Class-Separability (CS) strategy and Data Envelopment Analysis (DEA). To better capture the relationship between features and the class, class labels are separated into individual variables and relevance and redundancy are explicitly handle…
BERT learns claim descriptions to identify patent novelty.
Introduces a new feature importance measure using Gram-Schmidt decorrelation.
Interpretable machine learning uncovers ESG's explanatory power on equity returns across sectors and capitalizations.
While several feature scoring methods are proposed to explain the output of complex machine learning models, most of them lack formal mathematical definitions. In this study, we propose a novel definition of the feature score using the maximally invariant data perturbation, which is inspired from the idea of adversaria…
A method to improve clustering explainability using bagging and feature dropout.
We formalise the widespread idea of interpreting neural network decisions as an explicit optimisation problem in a rate-distortion framework. A set of input features is deemed relevant for a classification decision if the expected classifier score remains nearly constant when randomising the remaining features. We disc…
We propose a simple and efficient method for ranking features in multi-label classification. The method produces a ranking of features showing their relevance in predicting labels, which in turn allows to choose a final subset of features. The procedure is based on Markov Networks and allows to model the dependencies b…
Improved TF-IDF for word relevance in health-care social media documents.
New method identifies important features and interactions in RF models.
Layer-wise Relevance Propagation (LRP) and saliency maps have been recently used to explain the predictions of Deep Learning models, specifically in the domain of text classification. Given different attribution-based explanations to highlight relevant words for a predicted class label, experiments based on word deleti…
Framework detects out-of-distribution inputs in regression and survival analysis.
Before any publication, data analysis of high-energy physics experiments must be validated. This validation is granted only if a perfect understanding of the data and the analysis process is demonstrated. Therefore, physicists prefer using transparent machine learning algorithms whose performances highly rely on the su…
Structure based ligand discovery is one of the most successful approaches for augmenting the drug discovery process. Currently, there is a notable shift towards machine learning (ML) methodologies to aid such procedures. Deep learning has recently gained considerable attention as it allows the model to "learn" to extra…
Improves machine learning models by incorporating physical laws into feature maps.
Paper develops machine learning algorithms to learn optimal integer weights for clinical risk scores.
Efficiently predict LLM benchmarks using feature selection and regression.
CDAM improves attention maps for ViTs, making them more class-sensitive.
When estimating the relevancy between a query and a document, ranking models largely neglect the mutual information among documents. A common wisdom is that if two documents are similar in terms of the same query, they are more likely to have similar relevance score. To mitigate this problem, in this paper, we propose …
We propose a computationally efficient wrapper feature selection method - called Autoencoder and Model Based Elimination of features using Relevance and Redundancy scores (AMBER) - that uses a single ranker model along with autoencoders to perform greedy backward elimination of features. The ranker model is used to pri…
In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical. The performance metric used to compare different algorithms must also reflect the concerns of users in the application domain under consideration. In this work, we propose a novel probability-based performan…
Proposes Topology Distance for evaluating GANs.
Improves relevancy of black-box anomaly detectors with user feedback.
Fisher score is one of the most widely used supervised feature selection methods. However, it selects each feature independently according to their scores under the Fisher criterion, which leads to a suboptimal subset of features. In this paper, we present a generalized Fisher score to jointly select features. It aims …
EBBS integrates expert assessments into MIO best-subsets problem.
The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i.e. the prediction of ordered classes. Besides model accuracy, the interpretation of these models itself is of high relevance, and existing approaches therefore enforce e.g. model sparsity. For…
Advances in machine learning technologies have led to increasingly powerful models in particular in the context of big data. Yet, many application scenarios demand for robustly interpretable models rather than optimum model accuracy; as an example, this is the case if potential biomarkers or causal factors should be di…
A new method assigns anomaly scores to features for better interpretation.
New method disentangles feature importance scores in machine learning.
Identifies features most relevant to concept drift in data.
Research characterizes learnability of multilabel ranking problems.
New method distinguishes feature relevance in non-linear contexts.
Improved 3D ECG feature attributions for clinical interpretation.
Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks. For a given input sample, they assign a relevance score to each individual input variable, such as the pixels of an image. In this work we adapt the information bottleneck concept for attribution. B…
Fair MP-Boost improves fairness and interpretability in boosting methods.
MLS improves feature selection for imbalanced data.
Two new methods score stress test scenarios for risk managers.
The goal of feature selection is to identify important features that are relevant to explain an outcome variable. Most of the work in this domain has focused on identifying globally relevant features, which are features that are related to the outcome using evidence across the entire dataset. We study a more fine-grain…
For technology (like serious games) that aims to deliver interactive learning, it is important to address relevant mental experiences such as reflective thinking during problem solving. To facilitate research in this direction, we present the weDraw-1 Movement Dataset of body movement sensor data and reflective thinkin…