The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

Enhancing Confusion Entropy (CEN) for binary and multiclass classification

(2019) Rosario Delgado et al.   PLoS One

Computational prediction of diagnosis and feature selection on mesothelioma patient health records

(2019) Davide Chicco et al.   PLoS One

The impact of class imbalance in classification performance metrics based on the binary confusion matrix

(2019) Amalia Luque et al.   PATTERN RECOGNITION

Phylogenetic convolutional neural networks in metagenomics

(2018) Diego Fioravanti et al.   BMC BIOINFORMATICS

Multivariate comparison of classification performance measures

(2018) Davide Ballabio et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Classifiers and their Metrics Quantified

(2018) J. B. Brown   Molecular Informatics

Distillation of the clinical algorithm improves prognosis by multi-task deep learning in high-risk Neuroblastoma

(2018) Valerio Maggio et al.   PLoS One

Local epigenomic state cannot discriminate interacting and non-interacting enhancer–promoter pairs with high accuracy

(2018) Wang Xi et al.   PLoS Computational Biology

Designing multi-label classifiers that maximize F measures: State of the art

(2017) Ignazio Pillai et al.   PATTERN RECOGNITION

A note on using the F-measure for evaluating record linkage algorithms

(2017) David Hand et al.   STATISTICS AND COMPUTING

Precision and recall oncology: combining multiple gene mutations for improved identification of drug-sensitive tumours

(2017) Stefan Naulaerts et al.   Oncotarget

Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric

(2017) Sabri Boughorbel et al.   PLoS One

A Survey of Predictive Modeling on Imbalanced Domains

(2016) Paula Branco et al.   ACM COMPUTING SURVEYS

Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin

(2016) Sean Whalen et al.   NATURE GENETICS

The precision–recall curve overcame the optimism of the receiver operating characteristic curve in rare diseases

(2015) Brice Ozenne et al.   JOURNAL OF CLINICAL EPIDEMIOLOGY

Deep learning

(2015) Yann LeCun et al.   NATURE

The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets

(2015) Takaya Saito et al.   PLoS One

A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

(2014)   NATURE BIOTECHNOLOGY

The disagreeable behaviour of the kappa statistic

(2014) Laura Flight et al.   PHARMACEUTICAL STATISTICS

Area under Precision-Recall Curves for Weighted and Unweighted Data

(2014) Jens Keilwagen et al.   PLoS One

How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis

(2012) Mauno Vihinen   BMC GENOMICS

A Comparison of MCC and CEN Error Measures in Multi-Class Prediction

(2012) Giuseppe Jurman et al.   PLoS One

Pearson's correlation coefficient

(2012) P. Sedgwick   BMJ-British Medical Journal

Small-sample precision of ROC-related estimates

(2010) B. Hanczar et al.   BIOINFORMATICS

Class prediction for high-dimensional class-imbalanced data

(2010) Rok Blagus et al.   BMC BIOINFORMATICS

Random k-Labelsets for Multilabel Classification

(2010) Grigorios Tsoumakas et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

(2010)   NATURE BIOTECHNOLOGY

Predictor correlation impacts machine learning algorithms: implications for genomic studies

(2009) K. K. Nicodemus et al.   BIOINFORMATICS

A novel measure for evaluating classifiers

(2009) Jin-Mao Wei et al.   EXPERT SYSTEMS WITH APPLICATIONS

A systematic analysis of performance measures for classification tasks

(2009) Marina Sokolova et al.   INFORMATION PROCESSING & MANAGEMENT

Measuring classifier performance: a coherent alternative to the area under the ROC curve

(2009) David J. Hand   MACHINE LEARNING

An experimental comparison of performance measures for classification

(2008) C. Ferri et al.   PATTERN RECOGNITION LETTERS

Classification algorithms for phenotype prediction in genomics and proteomics

(2007) Habtom et al.   Frontiers in Bioscience-Landmark

AUC: a misleading measure of the performance of predictive distribution models

(2007) Jorge M. Lobo et al.   GLOBAL ECOLOGY AND BIOGEOGRAPHY

Comparison of classification accuracy using Cohen’s Weighted Kappa

(2006) A BENDAVID   EXPERT SYSTEMS WITH APPLICATIONS