| 1. |
- Kuratomi Hernandez, Alejandro, et al.
(författare)
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Ijuice : integer JUstIfied counterfactual explanations
- 2024
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Ingår i: Machine Learning. - 0885-6125 .- 1573-0565.
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Tidskriftsartikel (refereegranskat)abstract
- Counterfactual explanations modify the feature values of an instance in order to alter its prediction from an undesired to a desired label. As such, they are highly useful for providing trustworthy interpretations of decision-making in domains where complex and opaque machine learning algorithms are utilized. To guarantee their quality and promote user trust, they need to satisfy the faithfulness desideratum, when supported by the data distribution. We hereby propose a counterfactual generation algorithm for mixed-feature spaces that prioritizes faithfulness through k-justification, a novel counterfactual property introduced in this paper. The proposed algorithm employs a graph representation of the search space and provides counterfactuals by solving an integer program. In addition, the algorithm is classifier-agnostic and is not dependent on the order in which the feature space is explored. In our empirical evaluation, we demonstrate that it guarantees k-justification while showing comparable performance to state-of-the-art methods in feasibility, sparsity, and proximity.
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| 2. |
- Kuratomi Hernandez, Alejandro, et al.
(författare)
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Measuring the Burden of (Un)fairness Using Counterfactuals
- 2023
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Ingår i: Machine Learning and Principles and Practice of Knowledge Discovery in Databases. - Cham : Springer. - 9783031236174 ; , s. 402-417
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Konferensbidrag (refereegranskat)abstract
- In this paper, we use counterfactual explanations to offer a new perspective on fairness, that, besides accuracy, accounts also for the difficulty or burden to achieve fairness. We first gather a set of fairness-related datasets and implement a classifier to extract the set of false negative test instances to generate different counterfactual explanations on them. We subsequently calculate two measures: the false negative ratio of the set of test instances, and the distance (also called burden) from these instances to their corresponding counterfactuals, aggregated by sensitive feature groups. The first measure is an accuracy-based estimation of the classifier biases against sensitive groups, whilst the second is a counterfactual-based assessment of the difficulty each of these groups has of reaching their corresponding desired ground truth label. We promote the idea that a counterfactual and an accuracy-based fairness measure may assess fairness in a more holistic manner, whilst also providing interpretability. We then propose and evaluate, on these datasets, a measure called Normalized Accuracy Weighted Burden, which is more consistent than only its accuracy or its counterfactual components alone, considering both false negative ratios and counterfactual distance per sensitive feature. We believe this measure would be more adequate to assess classifier fairness and promote the design of better performing algorithms.
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| 3. |
- Lango Allen, Hana, et al.
(författare)
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Hundreds of variants clustered in genomic loci and biological pathways affect human height.
- 2010
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 467:7317, s. 832-8
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Tidskriftsartikel (refereegranskat)abstract
- Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P<0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.
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| 4. |
- Lindgren, Tony, 1974-
(författare)
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Methods of solving conflicts among induced rules
- 2006
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- When applying an unordered set of classification rules to classify an example, there may be several applicable rules with conflicting conclusions regarding the most probable class to which the example belongs. This problem of having rules assigning different classes to the same example must be addressed, if a classification is to be made. The standard methods of resolving such conflicts include using the most frequent class in the examples covered by the conflicting rules and using naive Bayes to calculate the most probable class.This thesis presents four papers, in each of which the problem of conflicting rules is addressed. In the first paper, a method that bridges the gap between Bayes rule and naive Bayes is presented. The second paper presents a data driven method for resolving rule conflicts, and the third paper explores this data driven approach further. In the last paper, a method for resolving rule conflicts in domains where the examples have numerical features is presented.For all new methods of solving rule conflicts, it is shown that the novel methods outperform the standard methods. A correlation between the novel methods performance and their computational cost is found: usually the more costly methods obtain a higher accuracy than the less costly methods.
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| 5. |
- Pavlopoulos, Ioannis, 1983-, et al.
(författare)
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Automotive fault nowcasting with machine learning and natural language processing
- 2024
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Ingår i: Machine Learning. - 0885-6125 .- 1573-0565. ; 113:2, s. 843-861
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Tidskriftsartikel (refereegranskat)abstract
- Automated fault diagnosis can facilitate diagnostics assistance, speedier troubleshooting, and better-organised logistics. Currently, most AI-based prognostics and health management in the automotive industry ignore textual descriptions of the experienced problems or symptoms. With this study, however, we propose an ML-assisted workflow for automotive fault nowcasting that improves on current industry standards. We show that a multilingual pre-trained Transformer model can effectively classify the textual symptom claims from a large company with vehicle fleets, despite the task’s challenging nature due to the 38 languages and 1357 classes involved. Overall, we report an accuracy of more than 80% for high-frequency classes and above 60% for classes with reasonable minimum support, bringing novel evidence that automotive troubleshooting management can benefit from multilingual symptom text classification.
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| 6. |
- Sun, Kaiji, et al.
(författare)
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Robust Contrastive Learning and Multi-shot Voting for High-dimensional Multivariate Data-driven Prognostics
- 2023
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Ingår i: 2023 IEEE International Conference on Prognostics and Health Management (ICPHM). - : IEEE (Institute of Electrical and Electronics Engineers). - 9798350346268 ; , s. 53-60
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Konferensbidrag (refereegranskat)abstract
- The availability of data gathered from industrial sensors has increased expeditiously in recent years. These data are valuable assets in delivering exceptional services for manufacturing enterprises. We see growing interests and expectations from manufacturers in deploying artificial intelligence for predictive maintenance. The paper has adopted and transferred a state-of-the-art method from few-shot learning to failure prognostics using the Siamese neural network based contractive learning. The method has three main characteristics on top of the highest performance - a sensitivity of 98.4% for Scania truck's air pressure system failure capture, compared to the methods proposed by the previous related research: prediction stability, deployment flexibility, and the robust multi-shot diagnosis based on selected historical reference samples
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