| 1. |
- Auffarth, Benjamin, et al.
(författare)
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Comparison of Redundancy and Relevance Measures for Feature Selection in Tissue Classification of CT images
- 2010
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Ingår i: Advances in Data Mining. - Heidelberg : Springer Berlin/Heidelberg. ; , s. 248-262
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Bokkapitel (refereegranskat)abstract
- In this paper we report on a study on feature selection within the minimum-redundancy maximum-relevance framework. Features are ranked by their correlations to the target vector. These relevance scores are then integrated with correlations between features in order to ob- tain a set of relevant and least-redundant features. Applied measures of correlation or distributional similarity for redundancy and relevance include Kolmogorov-Smirnov (KS) test, Spearman correlations, Jensen-Shannon divergence, and the sign-test. We introduce a metric called “value difference metric“ (VDM) and present a simple measure, which we call “fit criterion“ (FC). We draw conclusions about the usefulness of different measures. While KS-test and sign-test provided useful information, Spearman correlations are not fit for comparison of data of different measurement intervals. VDM was very good in our experiments as both redundancy and relevance measure. Jensen-Shannon and the sign-test are good redundancy measure alternatives and FC is a good relevance measure alternative.
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| 2. |
- Auffarth, Benjamin, et al.
(författare)
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Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images
- 2008
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Ingår i: Advances in Data Mining. - Heidelberg : Springer. ; , s. 16-31
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Bokkapitel (refereegranskat)abstract
- We study filter-based feature selection methods for classification of biomedical images. For feature selection, we use two filters - a relevance filter which measures usefulness of individual features for target prediction, and a redundancy filter, which measures similarity between features. As selection method that combines relevance and redundancy we try out a Hopfield network. We experimentally compare selection methods, running unitary redundancy and relevance filters, against a greedy algorithm with redundancy thresholds [9], the min-redundancy max-relevance integration [8,23,36], and our Hopfield network selection. We conclude that on the whole, Hopfield selection was one of the most successful methods, outperforming min-redundancy max-relevance when more features are selected.
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| 3. |
- Perez-Cerrolaza, Jon, et al.
(författare)
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Artificial Intelligence for Safety-Critical Systems in Industrial and Transportation Domains: A Survey
- 2024
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Ingår i: ACM Computing Surveys. - New York : Association for Computing Machinery. - 0360-0300 .- 1557-7341. ; 56:7
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Tidskriftsartikel (refereegranskat)abstract
- Artificial Intelligence (AI) can enable the development of next-generation autonomous safety-critical systems in which Machine Learning (ML) algorithms learn optimized and safe solutions. AI can also support and assist human safety engineers in developing safety-critical systems. However, reconciling both cutting-edge and state-of-the-art AI technology with safety engineering processes and safety standards is an open challenge that must be addressed before AI can be fully embraced in safety-critical systems. Many works already address this challenge, resulting in a vast and fragmented literature. Focusing on the industrial and transportation domains, this survey structures and analyzes challenges, techniques, and methods for developing AI-based safety-critical systems, from traditional functional safety systems to autonomous systems. AI trustworthiness spans several dimensions, such as engineering, ethics and legal, and this survey focuses on the safety engineering dimension.
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