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Related papers: Significativity Indices for Agreement Values

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Importance sampling is a common technique for Monte Carlo approximation, including Monte Carlo approximation of p-values. Here it is shown that a simple correction of the usual importance sampling p-values creates valid p-values, meaning…

Computation · Statistics 2011-04-12 Matthew T. Harrison

Text-based automated Cognitive Distortion detection is a challenging task due to its subjective nature, with low agreement scores observed even among expert human annotators, leading to unreliable annotations. We explore the use of Large…

Computation and Language · Computer Science 2026-05-21 Neha Sharma , Navneet Agarwal , Kairit Sirts

Constraint Acquisition (CA) and related research on the validation and enhancement of Mathematical Programming (MP) models from domain knowledge artifacts are currently limited by inadequate benchmarks. This deficiency impedes…

Artificial Intelligence · Computer Science 2026-05-27 Rafał Stachowiak , Tomasz P. Pawlak

Kendall rank correlation coefficient is used to measure the ordinal association between two measurements. In this paper, we introduce the Concordance coefficient as a generalization of the Kendall rank correlation, and illustrate its use to…

Methodology · Statistics 2020-11-13 Juan Francisco Monge

The comparison of benchmark error sets is an essential tool for the evaluation of theories in computational chemistry. The standard ranking of methods by their Mean Unsigned Error is unsatisfactory for several reasons linked to the…

Methodology · Statistics 2020-09-29 Pascal Pernot , Andreas Savin

In our chapter we address the statistical analysis of percentiles: How should the citation impact of institutions be compared? In educational and psychological testing, percentiles are already used widely as a standard to evaluate an…

Digital Libraries · Computer Science 2014-04-16 Richard Williams , Lutz Bornmann

This paper aims to showcase the measure of structural diversity of an ensemble of 9 classifiers and then map a relationship between this structural diversity and accuracy. The structural diversity was induced by having different…

Machine Learning · Computer Science 2008-05-01 Lesedi Masisi , Fulufhelo V. Nelwamondo , Tshilidzi Marwala

The objective of this paper is to assess the quality of explanation heatmaps for image classification tasks. To assess the quality of explainability methods, we approach the task through the lens of accuracy and stability. In this work, we…

Computer Vision and Pattern Recognition · Computer Science 2025-09-15 Lassi Raatikainen , Esa Rahtu

Graph classification aims to categorize graphs based on their structural and attribute features, with applications in diverse fields such as social network analysis and bioinformatics. Among the methods proposed to solve this task, those…

Machine Learning · Computer Science 2025-07-23 Lucas Potin , Rosa Figueiredo , Vincent Labatut , Christine Largeron

Current supervised deep learning frameworks rely on annotated data for modeling the underlying data distribution of a given task. In particular for computer vision algorithms powered by deep learning, the quality of annotated data is the…

Computer Vision and Pattern Recognition · Computer Science 2019-12-24 Joseph Nassar , Viveca Pavon-Harr , Marc Bosch , Ian McCulloh

Annotation quality is difficult to sustain when campaigns span weeks or months with small annotator pools. We present a Setswana sentiment dataset of 3,565 tweets annotated by three native-speaker annotators across eight batches and examine…

Associative Classifier is a novel technique which is the integration of Association Rule Mining and Classification. The difficult task in building Associative Classifier model is the selection of relevant rules from a large number of class…

Machine Learning · Computer Science 2010-03-25 S. Kannan , R. Bhaskaran

Outcome labeling ambiguity and subjectivity are ubiquitous in real-world datasets. While practitioners commonly combine ambiguous outcome labels for all data points (instances) in an ad hoc way to improve the accuracy of multi-class…

Machine Learning · Statistics 2022-07-05 Chihao Zhang , Yiling Elaine Chen , Shihua Zhang , Jingyi Jessica Li

We introduce a new information-theoretic formulation of quantum measurement uncertainty relations, based on the notion of relative entropy between measurement probabilities. In the case of a finite-dimensional system and for any approximate…

Mathematical Physics · Physics 2018-03-02 Alberto Barchielli , Matteo Gregoratti , Alessandro Toigo

Interpretable machine learning models offer understandable reasoning behind their decision-making process, though they may not always match the performance of their black-box counterparts. This trade-off between interpretability and model…

Artificial Intelligence · Computer Science 2025-03-12 Pranjal Atrey , Michael P. Brundage , Min Wu , Sanghamitra Dutta

The pairwise comparisons method is a convenient tool used when the relative order of preferences among different concepts (alternatives) needs to be determined. There are several popular implementations of this method, including the…

Discrete Mathematics · Computer Science 2018-02-08 Konrad Kułakowski

The paper proposes new second-order accuracy metrics for scoring or rating models, which show the target preference of the model, it is better to diagnose good objects or better to diagnose bad ones for a constant generally accepted…

Risk Management · Quantitative Finance 2023-01-19 M. V. Pomazanov

Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences. We propose to view text classification as a label-word joint embedding…

Computation and Language · Computer Science 2018-05-14 Guoyin Wang , Chunyuan Li , Wenlin Wang , Yizhe Zhang , Dinghan Shen , Xinyuan Zhang , Ricardo Henao , Lawrence Carin

Although a great methodological effort has been invested in proposing competitive solutions to the class-imbalance problem, little effort has been made in pursuing a theoretical understanding of this matter. In order to shed some light on…

Machine Learning · Statistics 2016-09-04 Jonathan Ortigosa-Hernández , Iñaki Inza , Jose A. Lozano

Recent work has sought to understand the behavior of neural networks by comparing representations between layers and between different trained models. We examine methods for comparing neural network representations based on canonical…

Machine Learning · Computer Science 2019-07-22 Simon Kornblith , Mohammad Norouzi , Honglak Lee , Geoffrey Hinton