Prediction confidence for associative classification

Do, .; *, Hui; Fong,

doi:10.1109/icmlc.2005.1527272

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…Table 7 show the effect of altering the minimum confidence of rules obtained from all datasets. Such results are in agreement with Do, Hui and Fong (2005), who state that a rule with a high confidence value implies an accurate prediction. However, as shown in Table 7, even though the AR increased simultaneously with the increment of minimum confidence values, the CR values decreased as a result.…”

Section: Data Partitionsupporting

confidence: 92%

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2014

APJITM

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Deriving useful and interesting rules from a data mining system is an essential and important task. Problems such as the discovery of random and coincidental patterns or patterns with no significant values, and the generation of a large volume of rules from a database commonly occur. Works on sustaining the interestingness of rules generated by data mining algorithms are actively and constantly being examined and developed. In this paper, a systematic way to evaluate the association rules discovered from frequent itemset mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriated sequence of usage is presented. The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided. Empirical results show that with a proper combination of data mining and statistical analysis, the framework is capable of eliminating a large number of non-significant, redundant and contradictive rules while preserving relatively valuable high accuracy and coverage rules when used in the classification problem. Moreover, the results reveal the important characteristics of mining frequent itemsets, and the impact of confidence measure for the classification task

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Section: Data Partitionsupporting

confidence: 92%

Untitled

2014

APJITM

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“…However, this may introduce challenges to identifiability and interpretation. In addition, to enhance scalability, alternatives to Gibbs sampling and adaptive Metropolis-Hastings could be exploited (Fong et al, 2019). Instead of consensus velocity, Bayesian stacking (Yao et al, 2022) is another option to explore multimodal posterior distribution that weights each chain differently.…”

Section: Discussionmentioning

confidence: 99%

Quantifying uncertainty in RNA velocity

Zhang,

Bochkina,

Wade

2024

Preprint

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The concept of RNA velocity has made it possible to extract dynamic information from single-cell RNA sequencing data snapshots, attracting considerable attention and inspiring various extensions. Nonetheless, existing approaches lack uncertainty quantification and many adopt unrealistic assumptions or employ complex black-box models that are difficult to interpret. In this paper, we present a Bayesian hierarchical model to estimate RNA velocity, which leverages a time-dependent transcription rate and non-trivial initial conditions, allowing for well-calibrated uncertainty quantification. The proposed method is validated in a comprehensive simulation study that covers various scenarios, and benchmarked against a widely embraced and commonly recognized approach for RNA velocity on single-cell RNA sequencing data from mouse embryonic stem cells. We demonstrate that our model surpasses this widely used, state-of-the-art method, offering enhanced interpretation of cell velocity and cell orders. Additionally, it supports the estimation of a unified gene-shared latent time, providing a valuable resource for downstream analysis.

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“…For a given test set D and a specific neuron n, the corresponding k-Multisection Neuron Coverage is defined as the ratio of the sections covered by D and the overall sections. It can be written as NBCOV [39] SNACOV [39] ALDp [38] ASS [40] PSD [41] MODEL CA AAW AAB ACAC [42] ACTC [42] NTE [41] MCE [43] RMCE [43] MFR [43] CAV [38] CRR/CSR [38] CCV [38] COS [38] EBD [44] EBD-2 ENI [44] NEURON SENSITIVITY [45] NEURON UNCERTAINTY…”

Section: A Data-oriented Evaluation Metricsmentioning

confidence: 99%

A Comprehensive Evaluation Framework for Deep Model Robustness

Liu¹,

Liu

Guo³

et al. 2021

Preprint

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Deep neural networks (DNNs) have achieved remarkable performance across a wide area of applications. However, they are vulnerable to adversarial examples, which motivates the adversarial defense. By adopting simple evaluation metrics, most of the current defenses only conduct incomplete evaluations, which are far from providing comprehensive understandings of the limitations of these defenses. Thus, most proposed defenses are quickly shown to be attacked successfully, which result in the "arm race" phenomenon between attack and defense. To mitigate this problem, we establish a model robustness evaluation framework containing a comprehensive, rigorous, and coherent set of evaluation metrics, which could fully evaluate model robustness and provide deep insights into building robust models. With 23 evaluation metrics in total, our framework primarily focuses on the two key factors of adversarial learning (i.e., data and model). Through neuron coverage and data imperceptibility, we use data-oriented metrics to measure the integrity of test examples; by delving into model structure and behavior, we exploit model-oriented metrics to further evaluate robustness in the adversarial setting. To fully demonstrate the effectiveness of our framework, we conduct large-scale experiments on multiple datasets including CIFAR-10 and SVHN using different models and defenses with our open-source platform AISafety 1 . Overall, our paper aims to provide a comprehensive evaluation framework which could demonstrate detailed inspections of the model robustness, and we hope that our paper can inspire further improvement to the model robustness.

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Prediction confidence for associative classification

Cited by 7 publications

References 3 publications

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Quantifying uncertainty in RNA velocity

A Comprehensive Evaluation Framework for Deep Model Robustness

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