Metrics for multi-class classification: an overview
Classification tasks in machine learning involving more than two classes are known by the
name of" multi-class classification". Performance indicators are very useful when the aim is …
name of" multi-class classification". Performance indicators are very useful when the aim is …
A survey on legal judgment prediction: Datasets, metrics, models and challenges
J Cui, X Shen, S Wen - IEEE Access, 2023 - ieeexplore.ieee.org
Legal judgment prediction (LJP) applies Natural Language Processing (NLP) techniques to
predict judgment results based on fact descriptions automatically. The present work …
predict judgment results based on fact descriptions automatically. The present work …
SignalP 6.0 predicts all five types of signal peptides using protein language models
Signal peptides (SPs) are short amino acid sequences that control protein secretion and
translocation in all living organisms. SPs can be predicted from sequence data, but existing …
translocation in all living organisms. SPs can be predicted from sequence data, but existing …
The Matthews correlation coefficient (MCC) is more informative than Cohen's Kappa and Brier score in binary classification assessment
Even if measuring the outcome of binary classifications is a pivotal task in machine learning
and statistics, no consensus has been reached yet about which statistical rate to employ to …
and statistics, no consensus has been reached yet about which statistical rate to employ to …
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
Background To evaluate binary classifications and their confusion matrices, scientific
researchers can employ several statistical rates, accordingly to the goal of the experiment …
researchers can employ several statistical rates, accordingly to the goal of the experiment …
[HTML][HTML] The impact of class imbalance in classification performance metrics based on the binary confusion matrix
A major issue in the classification of class imbalanced datasets involves the determination of
the most suitable performance metrics to be used. In previous work using several examples …
the most suitable performance metrics to be used. In previous work using several examples …
Modeling aspects of the language of life through transfer-learning protein sequences
Background Predicting protein function and structure from sequence is one important
challenge for computational biology. For 26 years, most state-of-the-art approaches …
challenge for computational biology. For 26 years, most state-of-the-art approaches …
DeepLoc: prediction of protein subcellular localization using deep learning
Motivation The prediction of eukaryotic protein subcellular localization is a well-studied topic
in bioinformatics due to its relevance in proteomics research. Many machine learning …
in bioinformatics due to its relevance in proteomics research. Many machine learning …
Why Cohen's Kappa should be avoided as performance measure in classification
We show that Cohen's Kappa and Matthews Correlation Coefficient (MCC), both extended
and contrasted measures of performance in multi-class classification, are correlated in most …
and contrasted measures of performance in multi-class classification, are correlated in most …
CovidXrayNet: Optimizing data augmentation and CNN hyperparameters for improved COVID-19 detection from CXR
To mitigate the spread of the current coronavirus disease 2019 (COVID-19) pandemic, it is
crucial to have an effective screening of infected patients to be isolated and treated. Chest X …
crucial to have an effective screening of infected patients to be isolated and treated. Chest X …