scholarly journals Multistep Fuzzy Bridged Refinement Domain Adaptation Algorithm and Its Application to Bank Failure Prediction

2015 ◽  
Vol 23 (6) ◽  
pp. 1917-1935 ◽  
Author(s):  
Vahid Behbood ◽  
Jie Lu ◽  
Guangquan Zhang ◽  
Witold Pedrycz
Keyword(s):  
Domain Adaptation ◽  
Failure Prediction ◽  
Bank Failure ◽  
Adaptation Algorithm

FUZZY BRIDGED REFINEMENT DOMAIN ADAPTATION: LONG-TERM BANK FAILURE PREDICTION

2013 ◽  
Vol 12 (01) ◽  
pp. 1350003 ◽  
Author(s):  
VAHID BEHBOOD ◽  
JIE LU ◽  
GUANGQUAN ZHANG
Keyword(s):  
Test Data ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Domain Adaptation ◽  
Failure Prediction ◽  
Bank Failure ◽  
Training Data ◽  
Support Vector ◽  
Target Domain

Machine learning methods, such as neural network (NN) and support vector machine, assume that the training data and the test data are drawn from the same distribution. This assumption may not be satisfied in many real world applications, like long-term financial failure prediction, because the training and test data may each come from different time periods or domains. This paper proposes a novel algorithm known as fuzzy bridged refinement-based domain adaptation to solve the problem of long-term prediction. The algorithm utilizes the fuzzy system and similarity concepts to modify the target instances' labels which were initially predicted by a shift-unaware prediction model. The experiments are performed using three shift-unaware prediction models based on nine different settings including two main situations: (1) there is no labeled instance in the target domain; (2) there are a few labeled instances in the target domain. In these experiments bank failure financial data is used to validate the algorithm. The results demonstrate a significant improvement in the predictive accuracy, particularly in the second situation identified above.

scholarly journals Does BERT need domain adaptation for clinical negation detection?

2020 ◽  
Vol 27 (4) ◽  
pp. 584-591 ◽  
Author(s):  
Chen Lin ◽  
Steven Bethard ◽  
Dmitriy Dligach ◽  
Farig Sadeque ◽  
Guergana Savova ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Domain Adaptation ◽  
Fine Tuning ◽  
Adaptation Algorithm ◽  
Learning Methods ◽  
Clinical Text ◽  
Unsupervised Domain Adaptation ◽  
Adversarial Training ◽  
Negation Detection ◽  
Adaptation Method

Abstract Introduction Classifying whether concepts in an unstructured clinical text are negated is an important unsolved task. New domain adaptation and transfer learning methods can potentially address this issue. Objective We examine neural unsupervised domain adaptation methods, introducing a novel combination of domain adaptation with transformer-based transfer learning methods to improve negation detection. We also want to better understand the interaction between the widely used bidirectional encoder representations from transformers (BERT) system and domain adaptation methods. Materials and Methods We use 4 clinical text datasets that are annotated with negation status. We evaluate a neural unsupervised domain adaptation algorithm and BERT, a transformer-based model that is pretrained on massive general text datasets. We develop an extension to BERT that uses domain adversarial training, a neural domain adaptation method that adds an objective to the negation task, that the classifier should not be able to distinguish between instances from 2 different domains. Results The domain adaptation methods we describe show positive results, but, on average, the best performance is obtained by plain BERT (without the extension). We provide evidence that the gains from BERT are likely not additive with the gains from domain adaptation. Discussion Our results suggest that, at least for the task of clinical negation detection, BERT subsumes domain adaptation, implying that BERT is already learning very general representations of negation phenomena such that fine-tuning even on a specific corpus does not lead to much overfitting. Conclusion Despite being trained on nonclinical text, the large training sets of models like BERT lead to large gains in performance for the clinical negation detection task.

scholarly journals Causal Embeddings for Recommendation: An Extended Abstract

2019 ◽  
Author(s):  
Flavian Vasile ◽  
Stephen Bonner
Keyword(s):  
Treatment Effect ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Adaptation Algorithm ◽  
Natural Behavior ◽  
New Approaches ◽  
Product Sales

Recommendations are commonly used to modify user’s natural behavior, for example, increasing product sales or the time spent on a website. This results in a gap between the ultimate business ob- jective and the classical setup where recommenda- tions are optimized to be coherent with past user be- havior. To bridge this gap, we propose a new learn- ing setup for recommendation that optimizes for the Incremental Treatment Effect (ITE) of the policy. We show this is equivalent to learning to predict recommendation outcomes under a fully random recommendation policy and propose a new domain adaptation algorithm that learns from logged data containing outcomes from a biased recommenda- tion policy and predicts recommendation outcomes according to random exposure. We compare our method against state-of-the-art factorization meth- ods, in addition to new approaches of causal rec- ommendation and show significant improvements.

scholarly journals Bias invariant RNA-seq metadata annotation

2020 ◽  
Author(s):  
Hannes Wartmann ◽  
Sven Heins ◽  
Karin Kloiber ◽  
Stefan Bonn
Keyword(s):  
Deep Learning ◽  
Domain Adaptation ◽  
Tissue Sample ◽  
Large Data ◽  
Biomedical Data ◽  
Rna Seq ◽  
Adaptation Algorithm ◽  
Data Repositories ◽  
Technological Advances ◽  
Metadata Annotation

AbstractRecent technological advances have resulted in an unprecedented increase in publicly available biomedical data, yet the reuse of the data is often precluded by experimental bias and a lack of annotation depth and consistency. Here we investigate RNA-seq metadata prediction based on gene expression values. We present a deep-learning based domain adaptation algorithm for the automatic annotation of RNA-seq metadata. We show how our algorithm outperforms existing approaches as well as traditional deep learning methods for the prediction of tissue, sample source, and patient sex information across several large data repositories. By using a model architecture similar to siamese networks the algorithm is able to learn biases from datasets with few samples. Our domain adaptation approach achieves metadata annotation accuracies up to 12.3% better than a previously published method. Lastly, we provide a list of more than 10,000 novel tissue and sex label annotations for 8,495 unique SRA samples.

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