A Study on the Relationships of Classifier Performance Metrics

Seliya, Naeem; Khoshgoftaar, Taghi M.; Hulse, Jason Van

doi:10.1109/ictai.2009.25

Cited by 189 publications

(85 citation statements)

References 12 publications

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“…For a binary classification problem, these performance measurements are based on the values of the confusion matrix: Number of True Positives (TP), number of True Negatives (TN), number of False Positives (FP) and number of False Negatives (FN) (Seliya et al, 2009). In this work, we used six performance metricsprecision, recall, specificity, efficiency, F-measure and G-measure -as follows.…”

Section: Metrics For Performance Evaluationmentioning

confidence: 99%

Statistical Pattern Recognition for Thresholding between Human Skin and Background in Color Images

Feitosa¹,

Santos²,

Oliveira³

et al. 2017

Journal of Computer Science

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Many research works based on the tone of human skin have been developed to locate and track the human body for the purpose of recognition in color images. With respect to other techniques, some advantages of face detection based on skin color are the smaller processing time, invariant angles of rotation and the performance in semi-occluded faces. In this study we present the results of a survey that investigated the performance of 4 supervised classifiers in skin detection. In order to maximize the generalization of the models, a training set containing samples of individuals of different ages and ethnicities was used. Experimental results showed that the best performance was achieved by using an ANN and the worst results were yielded by LDA. With the Naive Bayes, QDA and ANN algorithms, we showed that the white, black, yellow and brown tones of human skin are in a well-defined range of the RGB color spectrum determined by common characteristics. We also compiled 2798 skin samples for treatment and 305 images with their manually obtained labels as supplementary material, which was made available to help in the development of further research in human skin detection.

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Section: Metrics For Performance Evaluationmentioning

confidence: 99%

Statistical Pattern Recognition for Thresholding between Human Skin and Background in Color Images

Feitosa¹,

Santos²,

Oliveira³

et al. 2017

Journal of Computer Science

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“…Collect the Pi values into P Mutual Information [2], Kolmogorov-Smirnov statistic [24], Deviance [26], Geometric Mean [26], Area Under the ROC Curve [7], Area Under the Precision Recall Curve [24], and Signal-to-Noise [28]. Each of these techniques are filter-based feature ranking techniques.…”

Section: E Robust Rank Aggregationmentioning

confidence: 99%

An extensive comparison of feature ranking aggregation techniques in bioinformatics

Wald

Khoshgoftaar

Dittman

et al. 2012

2012 IEEE 13th International Conference on Information Reuse &Amp; Integration (IRI)

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Univariate feature rankers have been frequently used to order genes (features) in terms of their importance to a given bioinformatics challenge. Unfortunately, the resulting feature subsets tend to differ when applied to related (but distinct) datasets, or when applied to datasets which have been varied or corrupted in some fashion. As a result, a research focus has recently been on methods to measure or improve the stability of these feature subsets. One such method is called rank aggregation. Rank aggregation is the process of combining the information from several ranked lists (or in this case ordered gene lists) into a single more stable list. While there has been work on the creation of these methods, very little work has gone into comparing the lists generated by these techniques. Such a comparison allows for grouping the techniques into families, both for understanding how the families affect rank aggregation and for using less-computationally-expensive members of a given family. This paper is an extensive study on nine rank aggregation techniques across twenty-six bioinformatics datasets. Our results show that certain aggregation techniques are very similar to each other, while others are quite unique in that they are not similar to the other techniques. Additionally, it was found that as the size of the feature subset increases, the similarity between the techniques increases. To our knowledge this is the first study which examines this many rank aggregation techniques within the domain of bioinformatics.

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“…We will use the FNR and FPR as two of the performance measures in this study [22]. We have omitted the TNR and TPR since they can easily be computed from the FNR and FPR.…”

Section: Performance Measurementioning

confidence: 99%

A Dynamometer for an Ocean Turbine Prototype: Reliability through Automated Monitoring

Duhaney

Khoshgoftaar

Sloan

et al. 2011

2011 IEEE 13th International Symposium on High-Assurance Systems Engineering

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An ocean turbine extracts the kinetic energy from ocean currents to generate electricity. Machine Condition Monitoring (MCM) / Prognostic Health Monitoring (PHM) systems allow for self-checking and automated fault detection, and are integral in the construction of a highly reliable ocean turbine. This paper presents an onshore test platform for an ocean turbine as well as a case study showing how machine learning can be used to detect changes in the operational state of this plant based on its vibration signals.In the case study, seven widely used machine learners are trained on experimental data gathered from the test platform, a dynamometer, to detect changes in the machine's state. The classification models generated by these classifiers are being considered as possible components of the state detection module of an MCM/PHM system for ocean turbines, and would be used for fault prediction. Experimental results presented here show the effectiveness of decision tree and random forest learners on distinguishing between faulty and normal states based on vibration data preprocessed by a wavelet transform.

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A Study on the Relationships of Classifier Performance Metrics

Cited by 189 publications

References 12 publications

Statistical Pattern Recognition for Thresholding between Human Skin and Background in Color Images

Statistical Pattern Recognition for Thresholding between Human Skin and Background in Color Images

An extensive comparison of feature ranking aggregation techniques in bioinformatics

A Dynamometer for an Ocean Turbine Prototype: Reliability through Automated Monitoring

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