2,655 research outputs found
An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis
open access articleThis article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions
Bayesian belief networks for dementia diagnosis and other applications: a comparison of hand-crafting and construction using a novel data driven technique
The Bayesian network (BN) formalism is a powerful representation for
encoding domains characterised by uncertainty. However, before it
can be used it must first be constructed, which is a major challenge
for any real-life problem. There are two broad approaches, namely
the hand-crafted approach, which relies on a human expert, and the
data-driven approach, which relies on data. The former approach is
useful, however issues such as human bias can introduce errors into
the model. We have conducted a literature review of the
expert-driven approach, and we have cherry-picked a number of common
methods, and engineered a framework to assist non-BN experts with
expert-driven construction of BNs. The latter construction approach
uses algorithms to construct the model from a data set. However,
construction from data is provably NP-hard.
To solve this problem, approximate, heuristic algorithms have been
proposed; in particular, algorithms that assume an order between the
nodes, therefore reducing the search space. However, traditionally,
this approach relies on an expert providing the order among the
variables
--- an expert may not always be available, or may be unable to
provide the order. Nevertheless, if a good order is available, these
order-based algorithms have demonstrated good performance. More
recent approaches attempt to ``learn'' a good order then use the
order-based algorithm to discover the structure. To eliminate the
need for order information during construction, we propose a search
in the entire space of Bayesian network structures --- we present a
novel approach for carrying out this task, and we demonstrate its
performance against existing algorithms that search in the entire
space and the space of orders.
Finally, we employ the hand-crafting framework to construct models
for the task of diagnosis in a ``real-life'' medical domain,
dementia diagnosis. We collect real dementia data from clinical
practice, and we apply the data-driven algorithms developed to
assess the concordance between the reference models developed by
hand and the models derived from real clinical data
Inference of Genetic Regulatory Networks with Recurrent Neural Network Models using Particle Swarm Optimization
Genetic regulatory network inference is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. The availability of time series gene expression data makes it possible to investigate the gene activities of whole genomes, rather than those of only a pair of genes or among several genes. However, current computational methods do not sufficiently consider the temporal behavior of this type of data and lack the capability to capture the complex nonlinear system dynamics. We propose a recurrent neural network (RNN) and particle swarm optimization (PSO) approach to infer genetic regulatory networks from time series gene expression data. Under this framework, gene interaction is explained through a connection weight matrix. Based on the fact that the measured time points are limited and the assumption that the genetic networks are usually sparsely connected, we present a PSO-based search algorithm to unveil potential genetic network constructions that fit well with the time series data and explore possible gene interactions. Furthermore, PSO is used to train the RNN and determine the network parameters. Our approach has been applied to both synthetic and real data sets. The results demonstrate that the RNN/PSO can provide meaningful insights in understanding the nonlinear dynamics of the gene expression time series and revealing potential regulatory interactions between genes
Assessing hyper parameter optimization and speedup for convolutional neural networks
The increased processing power of graphical processing units (GPUs) and the availability of large image datasets has fostered a renewed interest in extracting semantic information from images. Promising results for complex image categorization problems have been achieved using deep learning, with neural networks comprised of many layers. Convolutional neural networks (CNN) are one such architecture which provides more opportunities for image classification. Advances in CNN enable the development of training models using large labelled image datasets, but the hyper parameters need to be specified, which is challenging and complex due to the large number of parameters. A substantial amount of computational power and processing time is required to determine the optimal hyper parameters to define a model yielding good results. This article provides a survey of the hyper parameter search and optimization methods for CNN architectures
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