Confusion Matrix Visualization

Susmaga, Robert

doi:10.1007/978-3-540-39985-8_12

Cited by 83 publications

(38 citation statements)

References 12 publications

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“…Finally, a confusion matrix is a specific table layout that allows visualization of the performance of an algorithm. Each row of the matrix represents the instances in an actual class, while each column represents the instances in a predicted class, or vice versa [55]. To compare pipeline patterns, we sought to identify common patterns in the best pipelines in the PipelineProfiler.…”

Section: Resultsmentioning

confidence: 99%

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Computer Vision and Machine Learning for Tuna and Salmon Meat Classification

2021

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Aquatic products are popular among consumers, and their visual quality used to be detected manually for freshness assessment. This paper presents a solution to inspect tuna and salmon meat from digital images. The solution proposes hardware and a protocol for preprocessing images and extracting parameters from the RGB, HSV, HSI, and L*a*b* spaces of the collected images to generate the datasets. Experiments are performed using machine learning classification methods. We evaluated the AutoML models to classify the freshness levels of tuna and salmon samples through the metrics of: accuracy, receiver operating characteristic curve, precision, recall, f1-score, and confusion matrix (CM). The ensembles generated by AutoML, for both tuna and salmon, reached 100% in all metrics, noting that the method of inspection of fish freshness from image collection, through preprocessing and extraction/fitting of features showed exceptional results when datasets were subjected to the machine learning models. We emphasize how easy it is to use the proposed solution in different contexts. Computer vision and machine learning, as a nondestructive method, were viable for external quality detection of tuna and salmon meat products through its efficiency, objectiveness, consistency, and reliability due to the experiments’ high accuracy.

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Section: Resultsmentioning

confidence: 99%

“…Support values represent the numbers of samples in each class. We evaluated the AutoML models to classify the freshness levels of tuna and salmon samples through the metrics: accuracy [50], receiver operating characteristic (ROC) curve [51], precision [52], recall [53], f1-score [54], and confusion matrix (CM) [55].…”

Section: Resultsmentioning

confidence: 99%

Computer Vision and Machine Learning for Tuna and Salmon Meat Classification

2021

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“…Each signal was recorded 20 times with various levels of additive white Gaussian noise. The total success of the training process was 99.2%, as can be seen from the generated confusion matrix [34] in Figure 11. In the confusion matrix, each cell on the diagonal of the matrix contains the number of machines that were correctly classified.…”

Section: Cpc Feature Extraction and Ann Training Proceduresmentioning

confidence: 93%

Harmonic Loads Classification by Means of Currents’ Physical Components

Beck

Machlev

2019

Energies

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Electric load identification and classification for smart grid environment can improve the power service for both consumers and producers. The main concept of electric load identification and classification is to disaggregate various loads and categorize them. In this paper, a new practical method for electric load identification and classification is presented. The method is based on using a power monitor to analyze a real measured current waveform of a grid-connected device. A set number of features is extracted using the currents’ physical components-based power theory decomposition. Using currents’ physical components ensures a constant number of features, which maintains the signal’s characteristics regardless of the harmonic content. These features are used to train a supervised classifier based on two techniques: artificial neural network and nearest neighbor search. The theory is outlined, and experimental results are shown. This paper demonstrates high accuracy performance in identifying an electric load from a designated database. Furthermore, the results show a definite classification of an untrained operation state of a device to the closest trained operation state, for example, the excitation angle of a dimmer. In a comparative study, the method is shown to outperform other state-of-the-art techniques, which are based on harmonic components.

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“…The K-fold cross validation method utilized to split the dataset into training and testing sets with 10-Folds, which helps to generalize the training and testing process on the whole dataset. Based on training and testing results, each model was evaluated based on the values generated by the confusion matrix [78], which is shown in Table 4. Then, the appropriate metrics were calculated [79], which are accuracy, precision, recall, and F1-score represented by Equations ( 4)- (7), respectively, for further comparison between classifiers' performance.…”

Section: 𝑍 = (𝑧 … 𝑧 … … … 𝑧 … 𝑧 )mentioning

confidence: 99%

Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches

Gharaibeh

Almahmoud

Ali

et al. 2021

BDCC

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Neuroimaging refers to the techniques that provide efficient information about the neural structure of the human brain, which is utilized for diagnosis, treatment, and scientific research. The problem of classifying neuroimages is one of the most important steps that are needed by medical staff to diagnose their patients early by investigating the indicators of different neuroimaging types. Early diagnosis of Alzheimer’s disease is of great importance in preventing the deterioration of the patient’s situation. In this research, a novel approach was devised based on a digital subtracted angiogram scan that provides sufficient features of a new biomarker cerebral blood flow. The used dataset was acquired from the database of K.A.U.H hospital and contains digital subtracted angiograms of participants who were diagnosed with Alzheimer’s disease, besides samples of normal controls. Since each scan included multiple frames for the left and right ICA’s, pre-processing steps were applied to make the dataset prepared for the next stages of feature extraction and classification. The multiple frames of scans transformed from real space into DCT space and averaged to remove noises. Then, the averaged image was transformed back to the real space, and both sides filtered with Meijering and concatenated in a single image. The proposed model extracts the features using different pre-trained models: InceptionV3 and DenseNet201. Then, the PCA method was utilized to select the features with 0.99 explained variance ratio, where the combination of selected features from both pre-trained models is fed into machine learning classifiers. Overall, the obtained experimental results are at least as good as other state-of-the-art approaches in the literature and more efficient according to the recent medical standards with a 99.14% level of accuracy, considering the difference in dataset samples and the used cerebral blood flow biomarker.

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Confusion Matrix Visualization

Cited by 83 publications

References 12 publications

Computer Vision and Machine Learning for Tuna and Salmon Meat Classification

Computer Vision and Machine Learning for Tuna and Salmon Meat Classification

Harmonic Loads Classification by Means of Currents’ Physical Components

Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches

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