2,471 research outputs found
Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction
of multiple target variables of diverse type. Due to its enormous application
potential, it has developed into an active and rapidly expanding research field
that combines several subfields of machine learning, including multivariate
regression, multi-label classification, multi-task learning, dyadic prediction,
zero-shot learning, network inference, and matrix completion. In this paper, we
present a unifying view on MTP problems and methods. First, we formally discuss
commonalities and differences between existing MTP problems. To this end, we
introduce a general framework that covers the above subfields as special cases.
As a second contribution, we provide a structured overview of MTP methods. This
is accomplished by identifying a number of key properties, which distinguish
such methods and determine their suitability for different types of problems.
Finally, we also discuss a few challenges for future research
Incremental Sparse Bayesian Ordinal Regression
Ordinal Regression (OR) aims to model the ordering information between
different data categories, which is a crucial topic in multi-label learning. An
important class of approaches to OR models the problem as a linear combination
of basis functions that map features to a high dimensional non-linear space.
However, most of the basis function-based algorithms are time consuming. We
propose an incremental sparse Bayesian approach to OR tasks and introduce an
algorithm to sequentially learn the relevant basis functions in the ordinal
scenario. Our method, called Incremental Sparse Bayesian Ordinal Regression
(ISBOR), automatically optimizes the hyper-parameters via the type-II maximum
likelihood method. By exploiting fast marginal likelihood optimization, ISBOR
can avoid big matrix inverses, which is the main bottleneck in applying basis
function-based algorithms to OR tasks on large-scale datasets. We show that
ISBOR can make accurate predictions with parsimonious basis functions while
offering automatic estimates of the prediction uncertainty. Extensive
experiments on synthetic and real word datasets demonstrate the efficiency and
effectiveness of ISBOR compared to other basis function-based OR approaches
Multivariate decoding of brain images using ordinal regression.
Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection
A Novel Hybrid Ordinal Learning Model with Health Care Application
Ordinal learning (OL) is a type of machine learning models with broad utility
in health care applications such as diagnosis of different grades of a disease
(e.g., mild, modest, severe) and prediction of the speed of disease progression
(e.g., very fast, fast, moderate, slow). This paper aims to tackle a situation
when precisely labeled samples are limited in the training set due to cost or
availability constraints, whereas there could be an abundance of samples with
imprecise labels. We focus on imprecise labels that are intervals, i.e., one
can know that a sample belongs to an interval of labels but cannot know which
unique label it has. This situation is quite common in health care datasets due
to limitations of the diagnostic instrument, sparse clinical visits, or/and
patient dropout. Limited research has been done to develop OL models with
imprecise/interval labels. We propose a new Hybrid Ordinal Learner (HOL) to
integrate samples with both precise and interval labels to train a robust OL
model. We also develop a tractable and efficient optimization algorithm to
solve the HOL formulation. We compare HOL with several recently developed OL
methods on four benchmarking datasets, which demonstrate the superior
performance of HOL. Finally, we apply HOL to a real-world dataset for
predicting the speed of progressing to Alzheimer's Disease (AD) for individuals
with Mild Cognitive Impairment (MCI) based on a combination of multi-modality
neuroimaging and demographic/clinical datasets. HOL achieves high accuracy in
the prediction and outperforms existing methods. The capability of accurately
predicting the speed of progression to AD for each individual with MCI has the
potential for helping facilitate more individually-optimized interventional
strategies.Comment: 16 pages, 3 figures, 2 table
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