Classification of Metageosystems by Ensembles of Machine Learning Models

Yamashkin, Stanislav A.; Yamashkin, Anatoliy A.; Radovanović, Milan; Petrović, Marko D.; Yamashkina, E. O.

doi:10.14445/22315381/ijett-v70i9p226

Cited by 3 publications

(3 citation statements)

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“…The different machine learning techniques were tested, obtaining efficient yet optimal comparisons. The proposed solution based on using ensemble methods of machine learning in a single metaclassifier [ 33 , 34 , 35 ] to control the quality assessment of food products increases the efficiency of model generalization compared to each of the component algorithms. Machine learning can contribute to improving quality standards, and controlling adulteration, as well as the efficiency of obtaining final products, i.e., blackcurrant powders.…”

Section: Introductionmentioning

confidence: 99%

Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles

Przybył,

Walkowiak,

Kowalczewski

2024

Foods

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In the modern times of technological development, it is important to select adequate methods to support various food and industrial problems, including innovative techniques with the help of artificial intelligence (AI). Effective analysis and the speed of algorithm implementation are key points in assessing the quality of food products. Non-invasive solutions are being sought to achieve high accuracy in the classification and evaluation of various food products. This paper presents various machine learning algorithm architectures to evaluate the efficiency of identifying blackcurrant powders (i.e., blackcurrant concentrate with a density of 67 °Brix and a color coefficient of 2.352 (E520/E420) in combination with the selected carrier) based on information encoded in microscopic images acquired via scanning electron microscopy (SEM). Recognition of blackcurrant powders was performed using texture feature extraction from images aided by the gray-level co-occurrence matrix (GLCM). It was evaluated for quality using individual single classifiers and a metaclassifier based on metrics such as accuracy, precision, recall, and F1-score. The research showed that the metaclassifier, as well as a single random forest (RF) classifier most effectively identified blackcurrant powders based on image texture features. This indicates that ensembles of classifiers in machine learning is an alternative approach to demonstrate better performance than the existing traditional solutions with single neural models. In the future, such solutions could be an important tool to support the assessment of the quality of food products in real time. Moreover, ensembles of classifiers can be used for faster analysis to determine the selection of an adequate machine learning algorithm for a given problem.

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Section: Introductionmentioning

confidence: 99%

Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles

Przybył,

Walkowiak,

Kowalczewski

2024

Foods

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“…Bagging and boosting techniques, such as Random Forest and Gradient Boosting Machines (GBM), have been widely employed in credit risk prediction [17]. Recent research has explored the benefits of using homogeneous and heterogeneous ensembles, where models from the same or different algorithm families are combined [8], [18], [19], [20], [21]. Researchers from various domains proved the effectiveness of ensemble techniques in their fields [22], [23], [24], [25], [26], [27], [28], [29], [30].…”

Section: Ensemble Techniquesmentioning

confidence: 99%

Explainable Multistage Ensemble 1D Convolutional Neural Network for Trust Worthy Credit Decision

Sugave

2024

IJACSA

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Banking is a dynamic industry that places significant importance on risk management, requiring accurate and interpretable AI models to make transparent lending decisions. This study introduces a groundbreaking approach that combines a multistage ensemble technique with a 1D convolutional neural network (CNN) architecture. The algorithm not only delivers superior classification performance but also offers interpretable explanations for its decisions. The algorithm is designed with multiple strategic steps to enhance model performance without sacrificing explainability. Thorough experiments were conducted using datasets from private banks and non-banking financial companies (NBFCs) in India to evaluate the algorithm's performance. It was compared against various state-of-the-art models, demonstrating remarkable precision, recall, F1 score, and accuracy values of 0.994, 0.992, 0.993, and 0.991, respectively. This outperformed competing models like homogeneous deep ensembles, 1D CNN, and Artificial Neural Networks (ANN). Furthermore, individual borrower dataset evaluations confirmed the proposed algorithm's consistency and efficiency, achieving precision, recall, F1 score, and accuracy values of 0.960, 0.961, 0.952, and 0.964, respectively. The research emphasizes the effectiveness of the explanatory integration decision process, wherein the Explainable Multistage Ensemble 1D CNN not only provides enhanced credit risk prediction but also facilitates transparent and interpretable lending decisions. The algorithm's ability to offer understandable explanations empowers financial institutions to make well-informed lending decisions, reduce credit risk, and foster a more stable and inclusive financial ecosystem.

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“…Machine learning (ML) and deep learning (DL) have received interest in the scientific community as well as industry [1][2][3][4][5][6][7]. Many applications of artificial intelligence methods can be found in many research areas, notably in engineering by monitoring the condition of structures in construction [8][9][10]; in robotics through autonomous robots equipped with artificial intelligence and vision systems, enabling objective assessment, reduced report generation time, and improved maintenance planning [11][12][13]; in medicine by performing diagnostic imaging, remote surgeries, surgical subtasks, and whole surgical procedures [14][15][16][17][18]; in finance for risk analysis, prediction, and maintenance planning of financial infrastructure [19][20][21]; and also in the agri-food industry [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Explainable AI: Machine Learning Interpretation in Blackcurrant Powders

Przybył

2024

Sensors

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Recently, explainability in machine and deep learning has become an important area in the field of research as well as interest, both due to the increasing use of artificial intelligence (AI) methods and understanding of the decisions made by models. The explainability of artificial intelligence (XAI) is due to the increasing consciousness in, among other things, data mining, error elimination, and learning performance by various AI algorithms. Moreover, XAI will allow the decisions made by models in problems to be more transparent as well as effective. In this study, models from the ‘glass box’ group of Decision Tree, among others, and the ‘black box’ group of Random Forest, among others, were proposed to understand the identification of selected types of currant powders. The learning process of these models was carried out to determine accuracy indicators such as accuracy, precision, recall, and F1-score. It was visualized using Local Interpretable Model Agnostic Explanations (LIMEs) to predict the effectiveness of identifying specific types of blackcurrant powders based on texture descriptors such as entropy, contrast, correlation, dissimilarity, and homogeneity. Bagging (Bagging_100), Decision Tree (DT0), and Random Forest (RF7_gini) proved to be the most effective models in the framework of currant powder interpretability. The measures of classifier performance in terms of accuracy, precision, recall, and F1-score for Bagging_100, respectively, reached values of approximately 0.979. In comparison, DT0 reached values of 0.968, 0.972, 0.968, and 0.969, and RF7_gini reached values of 0.963, 0.964, 0.963, and 0.963. These models achieved classifier performance measures of greater than 96%. In the future, XAI using agnostic models can be an additional important tool to help analyze data, including food products, even online.

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Classification of Metageosystems by Ensembles of Machine Learning Models

Cited by 3 publications

References 0 publications

Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles

Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles

Explainable Multistage Ensemble 1D Convolutional Neural Network for Trust Worthy Credit Decision

Explainable AI: Machine Learning Interpretation in Blackcurrant Powders

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