Using Published Medical Results and Non-homogenous Data in Rule Learning

Wojtusiak, Janusz; Irvin, Katherine; Birerdinc, Aybike; Baranova, Ancha

doi:10.1109/icmla.2011.154

Cited by 8 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instead, only aggregated summaries describing groups of patients are available. Such aggregated summaries are also one of the most common forms in which results are presented in medical publications [20].…”

Section: B Aggregated Vs Individual Valuesmentioning

confidence: 99%

See 1 more Smart Citation

Semantic Data Types in Machine Learning from Healthcare Data

Wojtusiak

2012

2012 11th International Conference on Machine Learning and Applications

View full text Add to dashboard Cite

Healthcare is particularly rich in semantic information and background knowledge describing data. This paper discusses a number of semantic data types that can be found in healthcare data, presents how the semantics can be extracted from existing sources including the Unified Medical Language System (UMLS), discusses how the semantics can be used in both supervised and unsupervised learning, and presents an example rule learning system that implements several of these types. Results from three example applications in the healthcare domain are used to further exemplify semantic data types.

show abstract

Section: B Aggregated Vs Individual Valuesmentioning

confidence: 99%

“…Aggregated data are the most common way of describing cohorts of patients [17] [19] [20]. Aggregated values of attributes are given in the form of pairs (μ A , σ A ), where A is a measured attribute, and μ A and σ A denote its mean and standard deviation measured over a group for which the aggregation was done.…”

Section: B Aggregated Vs Individual Valuesmentioning

confidence: 99%

Semantic Data Types in Machine Learning from Healthcare Data

Wojtusiak

2012

2012 11th International Conference on Machine Learning and Applications

View full text Add to dashboard Cite

show abstract

“…For example, this problem appears when predicting the proportion of votes for a given candidate (de Freitas & Kück, 2005); correctly predicting how each individual votes is not required, only which candidate will win. Variants of this problem also appear in many other domains, including in consumer marketing (Chen et al, 2006), medicine and other health domains (Hernández-González et al, 2013;Wojtusiak et al, 2011), image processing (de Freitas & Kück, 2005), physical processes (Musicant et al, 2007), fraud detection (Rüping, 2010), manufacturing (Stolpe & Morik, 2011), and voting networks (Fish et al, 2016). This problem may also arise when attempting to correct for differences between training and testing distributions (Du Plessis & Sugiyama, 2014;Saerens et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…Sometimes this is due to limits on the measurement process (Hernández-González et al, 2013;de Freitas & Kück, 2005;Musicant et al, 2007;Stolpe & Morik, 2011). At other times, before data sets are released, labels are purposely detached from their instances in order to maintain privacy (Chen et al, 2006;Rüping, 2010;Wojtusiak et al, 2011). Instead, only the proportion of labels are given for a group of sample instances.…”

Section: Introductionmentioning

confidence: 99%

On the Complexity of Learning a Class Ratio from Unlabeled Data

Fish¹,

Reyzin²

2020

jair

View full text Add to dashboard Cite

In the problem of learning a class ratio from unlabeled data, which we call CR learning, the training data is unlabeled, and only the ratios, or proportions, of examples receiving each label are given. The goal is to learn a hypothesis that predicts the proportions of labels on the distribution underlying the sample. This model of learning is applicable to a wide variety of settings, including predicting the number of votes for candidates in political elections from polls. In this paper, we formally define this class and resolve foundational questions regarding the computational complexity of CR learning and characterize its relationship to PAC learning. Among our results, we show, perhaps surprisingly, that for finite VC classes what can be efficiently CR learned is a strict subset of what can be learned efficiently in PAC, under standard complexity assumptions. We also show that there exist classes of functions whose CR learnability is independent of ZFC, the standard set theoretic axioms. This implies that CR learning cannot be easily characterized (like PAC by VC dimension).

show abstract

“…Sometimes this is due to limits on the measurement process [7,4,11,17]. At other times, before datasets are released, labels are purposely detached from their instances in order to maintain privacy [3,15,19]. Instead, only the proportion of labels are given for a group of sample instances.…”

Section: Introductionmentioning

confidence: 99%

On the Complexity of Learning from Label Proportions

Fish

Reyzin

2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

View full text Add to dashboard Cite

In the problem of learning with label proportions (also known as the problem of estimating class ratios), the training data is unlabeled, and only the proportions of examples receiving each label are given. The goal is to learn a hypothesis that predicts the proportions of labels on the distribution underlying the sample. This model of learning is useful in a wide variety of settings, including predicting the number of votes for candidates in political elections from polls. In this paper, we resolve foundational questions regarding the computational complexity of learning in this setting. We formalize a simple version of the setting, and we compare the computational complexity of learning in this model to classical PAC learning. Perhaps surprisingly, we show that what can be learned efficiently in this model is a strict subset of what may be leaned efficiently in PAC, under standard complexity assumptions. We give a characterization in terms of VC dimension, and we show that there are non-trivial problems in this model that can be efficiently learned. We also give an algorithm that demonstrates the feasibility of learning under well-behaved distributions.

show abstract

Using Published Medical Results and Non-homogenous Data in Rule Learning

Cited by 8 publications

References 12 publications

Semantic Data Types in Machine Learning from Healthcare Data

Semantic Data Types in Machine Learning from Healthcare Data

On the Complexity of Learning a Class Ratio from Unlabeled Data

On the Complexity of Learning from Label Proportions

Contact Info

Product

Resources

About