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CoSP: co-selection pick for a global explainability of black box machine learning models.

, , , , , , and . World Wide Web (WWW), 26 (6): 3965-3981 (November 2023)

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A Novel Proposed Pooling for Convolutional Neural Network., , , and . ICTAI, page 1739-1743. IEEE, (2019)CoSP: co-selection pick for a global explainability of black box machine learning models., , , , , , and . World Wide Web (WWW), 26 (6): 3965-3981 (November 2023)Reducing Traffic Congestion by LSTM-LOF Framework., , , , and . AICCSA, page 1-8. IEEE Computer Society, (2019)Adaptive Patch Labeling and Multi-Label Feature Selection for 360-Degree Image Quality Assessment., , and . MMSP, page 1-6. IEEE, (2023)An Improved Laplacian Semi-Supervised Regression., , , and . ICTAI, page 564-570. IEEE, (2018)The Mode-Fisher pooling for time complexity optimization in deep convolutional neural networks., , , and . Neural Comput. Appl., 33 (12): 6443-6465 (2021)Towards a Co-selection Approach for a Global Explainability of Black Box Machine Learning Models., , , , , , and . WISE, volume 13724 of Lecture Notes in Computer Science, page 97-109. Springer, (2022)SRDF_QDAG: An efficient end-to-end RDF data management when graph exploration meets spatial processing., , , , and . Comput. Sci. Inf. Syst., 20 (4): 1311-1341 (2023)ℓ2-type regularization-based unsupervised anomaly detection from temporal data., , and . IJCNN, page 2354-2361. IEEE, (2017)RDF_QDAG in Action: Efficient RDF Data Querying at Scale., , , , , and . WISE, volume 13724 of Lecture Notes in Computer Science, page 633-640. Springer, (2022)