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Unimodal regularized neuron stick-breaking for ordinal classification
- Source :
- Neurocomputing. 388:34-44
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- This paper targets for the ordinal regression/classification, which objective is to learn a rule to predict labels from a discrete but ordered set. For instance, the classification for medical diagnosis usually involves inherently ordered labels corresponding to the level of health risk. Previous multi-task classifiers on ordinal data often use several binary classification branches to compute a series of cumulative probabilities. However, these cumulative probabilities are not guaranteed to be monotonically decreasing. It also introduces a large number of hyper-parameters to be fine-tuned manually. This paper aims to eliminate or at least largely reduce the effects of those problems. We propose a simple yet efficient way to rephrase the output layer of the conventional deep neural network. Besides, in order to alleviate the effects of label noise in ordinal datasets, we propose a unimodal label regularization strategy. It also explicitly encourages the class predictions to distribute on nearby classes of ground truth. We show that our methods lead to the state-of-the-art accuracy on the medical diagnose task (e.g., Diabetic Retinopathy and Ultrasound Breast dataset) as well as the face age prediction (e.g., Adience face and MORPH Album II) with very little additional cost.
- Subjects :
- Ordinal data
0209 industrial biotechnology
Class (set theory)
Artificial neural network
Computer science
business.industry
Cognitive Neuroscience
Monotonic function
Pattern recognition
02 engineering and technology
Ordinal regression
Computer Science Applications
ComputingMethodologies_PATTERNRECOGNITION
020901 industrial engineering & automation
Binary classification
Artificial Intelligence
Face (geometry)
0202 electrical engineering, electronic engineering, information engineering
Ordinal number
020201 artificial intelligence & image processing
Artificial intelligence
business
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 388
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........1ea635f52910257405718b5b88062927