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Skip to Search Results- 4Explainable AI
- 2Machine Learning
- 1Artificial Intelligence
- 1Associative Classification
- 1Associative Classification
- 1Breast Cancer Biomarker Prediction
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Fall 2020
The power of associative classifiers is to determine patterns from the data and perform classification based on the features that are most indicative for prediction. Although they have emerged as competitive classification systems, however, they suffer limitations such as without prior knowledge...
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Improving Breast Cancer Biomarker Predictors from Morphology via Corresponding Gaussian Processes
DownloadFall 2024
Hosseini Akbarnejad, Amir Hossein
Biomarkers for cancer are tests performed on tumoral tissue which extract information from genes (DNA, deoxyribonucleic acid), product of genes (RNA, ribonucleic acid) and proteins. The information obtained from biomarkers (abnormal amount, strutural defect, etc.) is the basis for breast cancer...
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Spring 2023
With machine learning models becoming more complicated and more widely applied to solve real-world challenges, there comes the need to explain their reasoning. In parallel with the advancements of deep learning methods, Explainable AI (XAI) algorithms have been proposed to address the issue of...
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Fall 2023
Giving reasons for justifying the decisions made by classification models has received less attention in recent artificial intelligence breakthroughs than improving the accuracy of the models. Recently, AI researchers are paying more attention to filling this gap, leading to the introduction of...