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

Jurman, Giuseppe; Riccadonna, Samantha; Furlanello, Cesare

doi:10.1371/journal.pone.0041882

Cited by 325 publications

(185 citation statements)

References 29 publications

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“…The MCC measure was originally extended to the multi-class problem in [14]. Recently, and following a comparison between MCC and Confusion Entropy [40] reported in [17], MCC was recommended as an optimal tool for practical tasks, since it presents a good trade-off among discriminatory ability, consistency and coherent behavior with varying number of classes, unbalanced datasets and randomization.…”

Section: Performance Assessment Measuresmentioning

confidence: 99%

The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors

Cruz-Barbosa

Vellido

Giraldo

2014

Med Biol Eng Comput

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G protein-coupled receptors (GPCRs) are integral cell membrane proteins of relevance for pharmacology. The tertiary structure of the transmembrane domain, a gate to the study of protein functionality, is unknown for almost all members of class C GPCRs, which are the target of the current study. As a result, their investigation must often rely on alignments of their amino acid sequences. Sequence alignment entails the risk of missing relevant information. Various approaches have attempted to circumvent this risk through alignment-free transformations of the sequences on the basis of different amino acid physicochemical properties. In this paper, we use several of these alignment-free methods, as well as a basic amino acid composition representation, to transform the available sequences. Novel semi-supervised statistical machine learning methods are then used to discriminate the different class C GPCRs types from the transformed data. This approach is relevant due to the existence of orphan proteins to which type labels should be assigned in a process of deorphanization or reverse pharmacology. The reported experiments show that the proposed techniques provide accurate classification even in settings of extreme class-label scarcity and that fair accuracy can be achieved even with very simple transformation strategies that ignore the sequence ordering.

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Section: Performance Assessment Measuresmentioning

confidence: 99%

The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors

Cruz-Barbosa

Vellido

Giraldo

2014

Med Biol Eng Comput

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“…Several studies related to data mining and classification have been established to propose and compare various classifiers and to show their prediction quality and performance [26] [27], [28]. Jurman et al [29] shows that the Matthews Correlation Coefficient (MCC) method gives reliable results about the quality of binary and multi-class prediction classifiers. Trust evaluation models are a kind of classifier where agents are classified based on their trustworthiness.…”

Section: Overall Architecturementioning

confidence: 99%

Towards a Framework for the Analysis and Evaluation of Computational Trust Models in Multi-agent Systems

Mifrah¹,

En‐Nouaary²,

Dahchour³

2017

JSW

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Abstract:In open dynamic multi-agent systems, trust is commonly considered as a critical concept to be handled and managed. Computational trust models are a kinds of formal models that have been proposed to manage trust in such situation. These models present a new form of distributed intelligence in virtual societies and collective intelligence. However, the diversity of those models makes user confused about which one to choose. Different testbeds have been established to evaluate trust and reputation models and verify their robustness and efficiency. However, a lack of flexibility to handle scenarios related to multi-context trust models arise with those testbeds. We present in this paper a framework for evaluating computational trust models that provides to users more flexibility while comparing trust models in open systems and shows analysis results in chart diagrams. The ultimate objective is to evaluate and classify available computational trust models.

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“…The MCC is calculated from the confusion matrix [34]. The FFS was initialized by training a predictor using each feature separately.…”

Section: Feature Selection and Trainingmentioning

confidence: 99%

Perceived Audio Quality of Sounds Degraded by Nonlinear Distortions and Single-Ended Assessment Using HASQI

Kendrick¹,

al.²

2015

J. Audio Eng. Soc.

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For field recordings and user generated content recorded on phones, tablets, and other mobile devices nonlinear distortions caused by clipping and limiting at pre-amplification stages, and dynamic range control (DRC) are common causes of poor audio quality. A single-ended method to detect these distortions and predict perceived degradation in speech, music, and soundscapes has been developed. This was done by training an ensemble of decision trees. During training, both clean and distorted audio was available and so the perceived quality could be gauged using HASQI (Hearing Aid Sound Quality Index). The new single-ended method can correctly predict HASQI from distorted samples to an accuracy of ±0.19 (95% confidence interval) using a quality range between 0.0 and 1.0. The method also has potential for estimating HASQI when other types of degradations are present. Subsequent perceptual tests validated the method for music and soundscapes. For the average mean opinion score for perceived audio quality on a scale from 0 to 1, the single ended method could estimate it within ±0.33.

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

Cited by 325 publications

References 29 publications

The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors

The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors

Towards a Framework for the Analysis and Evaluation of Computational Trust Models in Multi-agent Systems

Perceived Audio Quality of Sounds Degraded by Nonlinear Distortions and Single-Ended Assessment Using HASQI

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