Comparing two SVM models through different metrics based on the confusion matrix

Valero-Carreras, Daniel; Alcaraz, Javier; Landete, Mercedes

doi:10.1016/j.cor.2022.106131

Cited by 71 publications

(34 citation statements)

References 38 publications

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“…Through a comparison of the predicted and true classes, it demonstrates the number of correct and wrong predictions for each class. This information may be utilized to make decisions and optimize our algorithms [79]. These results were comparable to the testing accuracy of 99% recorded by Albarrak [80] for date fruit classification.…”

Section: Cnn Performancessupporting

confidence: 78%

The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning

Sadimantara,

Argo,

Sucipto

et al. 2024

Journal of Robotics and Control

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Aflatoxin contamination in cacao is a significant problem in terms of trade losses and health effects. This calls for the need for a non-invasive, precise, and effective detection strategy. This research contribution is to determine the best deep-learning model to classify the aflatoxin contamination level in cocoa beans based on fluorescence images and deep learning to improve performance in the classification. The process involved inoculating and incubating Aspergillus flavus (6mL/100g) to obtain aflatoxin-contaminated cocoa beans for 7 days during the incubation period. Liquid Mass Chromatography (LCMS) was used to quantify the aflatoxin in order to categorize the images into different levels including “free of aflatoxin”, “contaminated below the limit”, and “contaminated above the limit”. 300 images were acquired through a mini studio equipped with UV lamps. The aflatoxin level was classified using several pre-trained CNN approaches which has high accuracy such as GoogLeNet, SqueezeNet, AlexNet, and ResNet50. The sensitivity analysis showed that the highest classification accuracy was found in the GoogLeNet model with optimizer: Adam and learning rate: 0.0001 by 96.42%. The model was tested using a testing dataset and obtain accuracy of 96% based on the confusion matrix. The findings indicate that combining CNN with fluorescence images improved the ability to classify the amount of aflatoxin contamination in cacao beans. This method has the potential to be more accurate and economical than the current approach, which could be adapted to reduce aflatoxin's negative effects on food safety and cacao trade losses.

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Section: Cnn Performancessupporting

confidence: 78%

The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning

Sadimantara,

Argo,

Sucipto

et al. 2024

Journal of Robotics and Control

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“…In the confusion matrix in a multiclassification model, TP represents the actual case, that is, the number of samples in which the model correctly classifies positive cases as positive cases; TN represents real negative cases, that is, the number of pieces in which the model correctly classifies negative cases as negative cases; FP describes false positive cases, that is, the number of elements in which the model incorrectly classifies negative cases as positive cases; FN represents false negative cases, that is, the number of pieces in which the model incorrectly classifies positive cases as negative cases. The evaluation indicators are calculated as follows [24]…”

Section: Evaluation Index Of Adversarial Domain Adaptation For Grindi...mentioning

confidence: 99%

Recognition of grinding surface roughness grade based on adversarial domain adaptation under variable illumination

Yi,

Huang,

Shu

et al. 2024

Surf. Topogr.: Metrol. Prop.

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Deep learning can realize the self-extraction of grinding surface features so that end-to-end roughness measurement can be realized. Still, due to the grinding surface texture being random, the features are weak, the self-extracted grinding surface features of the same surface under different lighting environments are different, and the training data and the test data when the lighting environments are inconsistent with the recognition of the measurement of the accuracy of the lower. To address these issues, this paper proposes an adversarial domain self-adaptation (NMDANN) based visual measurement method for grinding surface roughness under variable illumination. An improved residual network is used as a generator to extract more effective metastable features, and multi-head attention is introduced into the domain discriminator to enhance its domain adaptive capability. The experimental results show that the method can recognize different grades of roughness on the grinding surface under changing light environments, laying the foundation for online visual measurement of grinding surface roughness under variable light environments.

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“…Then, the data set is divided into three different subsets: a training set, a validation set, and a test set. Selection of feature subset and ML algorithm: The minimum redundancy maximum relevance (MRMR) algorithm is used to rank all candidate features, and the optimal feature subset and ML algorithm are determined. Model optimization: The hyper-parameters of the selected optimal ML algorithm (the k- nearest neighbor algorithm, k- NN) are further adjusted using the Bayesian optimization algorithm (BOA) with a cost-sensitive learning approach Model evaluation: The model’s performance is comprehensively evaluated using confusion matrixes Model Interpretation: The local interpretable model-agnostic explanations (LIME) and partial dependence plots (PDP) methods are used to interpret the model, and to understand the effect of different features on predictions. …”

Section: Developing An Interpretable Optimal K-nn Model To Classify T...mentioning

confidence: 99%

“…For the sake of further explanation, Figures – present the confusion matrices of all sets used for training, validation and testing. In the matrix, the two green squares show correctly classified samples, while the two red squares express misclassified samples.…”

Section: Developing An Interpretable Optimal K-nn Model To Classify T...mentioning

confidence: 99%

Interpretable Machine Learning-Based Model to Classify and Optimize Isoperibolic Batch Reactors

Wen,

Lyu,

Zhang

et al. 2023

Ind. Eng. Chem. Res.

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It is crucial to classify and optimize the thermal behaviors of isoperibolic batch reactors. This article aims to construct an improved supervised machine learning-based (ML-based) model to achieve the task. First, using the minimum redundancy maximum relevance algorithm, the k-nearest neighbor (k-NN) algorithm with a feature subset consisting of 18 dimensions is selected due to the highest total recognition accuracy in 27 different ML algorithms. Additionally, a cost-sensitive learning approach and Bayesian optimization algorithm are employed to further optimize the hyper-parameters of the k-NN model. The accuracies of all data sets using the optimal k-NN model are all 99.8%, indicating that the optimal k-NN model has a superior performance and a good generalization ability. Then, two cases coupled with interpretability techniques are used to interpret the optimal k-NN model. Finally, based on the optimal k-NN model, two novel optimization frameworks (single-objective and multiobjective) are proposed to optimize the mentioned pilot-scale case, and the results prove that the optimization frameworks are reasonable and reliable.

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Comparing two SVM models through different metrics based on the confusion matrix

Cited by 71 publications

References 38 publications

The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning

The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning

Recognition of grinding surface roughness grade based on adversarial domain adaptation under variable illumination

Interpretable Machine Learning-Based Model to Classify and Optimize Isoperibolic Batch Reactors

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