3,243 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
An Improved Bees Algorithm for Training Deep Recurrent Networks for Sentiment Classification
Recurrent neural networks (RNNs) are powerful tools for learning information from
temporal sequences. Designing an optimum deep RNN is difficult due to configuration and training
issues, such as vanishing and exploding gradients. In this paper, a novel metaheuristic optimisation
approach is proposed for training deep RNNs for the sentiment classification task. The approach
employs an enhanced Ternary Bees Algorithm (BA-3+), which operates for large dataset classification
problems by considering only three individual solutions in each iteration. BA-3+ combines the
collaborative search of three bees to find the optimal set of trainable parameters of the proposed deep
recurrent learning architecture. Local learning with exploitative search utilises the greedy selection
strategy. Stochastic gradient descent (SGD) learning with singular value decomposition (SVD) aims to
handle vanishing and exploding gradients of the decision parameters with the stabilisation strategy
of SVD. Global learning with explorative search achieves faster convergence without getting trapped
at local optima to find the optimal set of trainable parameters of the proposed deep recurrent learning
architecture. BA-3+ has been tested on the sentiment classification task to classify symmetric and
asymmetric distribution of the datasets from different domains, including Twitter, product reviews,
and movie reviews. Comparative results have been obtained for advanced deep language models and
Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. BA-3+ converged
to the global minimum faster than the DE and PSO algorithms, and it outperformed the SGD, DE,
and PSO algorithms for the Turkish and English datasets. The accuracy value and F1 measure have
improved at least with a 30â40% improvement than the standard SGD algorithm for all classification
datasets. Accuracy rates in the RNN model trained with BA-3+ ranged from 80% to 90%, while the
RNN trained with SGD was able to achieve between 50% and 60% for most datasets. The performance
of the RNN model with BA-3+ has as good as for Tree-LSTMs and Recursive Neural Tensor Networks
(RNTNs) language models, which achieved accuracy results of up to 90% for some datasets. The
improved accuracy and convergence results show that BA-3+ is an efficient, stable algorithm for the
complex classification task, and it can handle the vanishing and exploding gradients problem of
deep RNNs
Nature-inspired algorithms for solving some hard numerical problems
Optimisation is a branch of mathematics that was developed to find the optimal solutions,
among all the possible ones, for a given problem. Applications of optimisation techniques
are currently employed in engineering, computing, and industrial problems. Therefore, optimisation is a very active research area, leading to the publication of a large number of
methods to solve specific problems to its optimality.
This dissertation focuses on the adaptation of two nature inspired algorithms that, based
on optimisation techniques, are able to compute approximations for zeros of polynomials
and roots of non-linear equations and systems of non-linear equations.
Although many iterative methods for finding all the roots of a given function already
exist, they usually require: (a) repeated deflations, that can lead to very inaccurate results
due to the problem of accumulating rounding errors, (b) good initial approximations to the
roots for the algorithm converge, or (c) the computation of first or second order derivatives,
which besides being computationally intensive, it is not always possible.
The drawbacks previously mentioned served as motivation for the use of Particle Swarm
Optimisation (PSO) and Artificial Neural Networks (ANNs) for root-finding, since they are
known, respectively, for their ability to explore high-dimensional spaces (not requiring good
initial approximations) and for their capability to model complex problems. Besides that,
both methods do not need repeated deflations, nor derivative information.
The algorithms were described throughout this document and tested using a test suite of
hard numerical problems in science and engineering. Results, in turn, were compared with
several results available on the literature and with the well-known DurandâKerner method,
depicting that both algorithms are effective to solve the numerical problems considered.A Optimização Ă© um ramo da matemĂĄtica desenvolvido para encontrar as soluçÔes Ăłptimas, de entre todas as possĂveis, para um determinado problema. Actualmente, sĂŁo vĂĄrias as
tĂ©cnicas de optimização aplicadas a problemas de engenharia, de informĂĄtica e da indĂșstria.
Dada a grande panĂłplia de aplicaçÔes, existem inĂșmeros trabalhos publicados que propĂ”em
mĂ©todos para resolver, de forma Ăłptima, problemas especĂficos.
Esta dissertação foca-se na adaptação de dois algoritmos inspirados na natureza que,
tendo como base técnicas de optimização, são capazes de calcular aproximaçÔes para zeros
de polinĂłmios e raĂzes de equaçÔes nĂŁo lineares e sistemas de equaçÔes nĂŁo lineares.
Embora jĂĄ existam muitos mĂ©todos iterativos para encontrar todas as raĂzes ou zeros de
uma função, eles usualmente exigem: (a) deflaçÔes repetidas, que podem levar a resultados
muito inexactos, devido ao problema da acumulação de erros de arredondamento a cada
iteração; (b) boas aproximaçÔes iniciais para as raĂzes para o algoritmo convergir, ou (c) o
cålculo de derivadas de primeira ou de segunda ordem que, além de ser computacionalmente
intensivo, para muitas funçÔes Ă© impossĂvel de se calcular.
Estas desvantagens motivaram o uso da Optimização por Enxame de PartĂculas (PSO) e
de Redes Neurais Artificiais (RNAs) para o cĂĄlculo de raĂzes. Estas tĂ©cnicas sĂŁo conhecidas,
respectivamente, pela sua capacidade de explorar espaços de dimensão superior (não exigindo
boas aproximaçÔes iniciais) e pela sua capacidade de modelar problemas complexos. Além
disto, tais técnicas não necessitam de deflaçÔes repetidas, nem do cålculo de derivadas.
Ao longo deste documento, os algoritmos sĂŁo descritos e testados, usando um conjunto de
problemas numĂ©ricos com aplicaçÔes nas ciĂȘncias e na engenharia. Os resultados foram comparados com outros disponĂveis na literatura e com o mĂ©todo de DurandâKerner, e sugerem
que ambos os algoritmos são capazes de resolver os problemas numéricos considerados
Evolving and Ensembling Deep CNN Architectures for Image Classification
Deep Convolutional Neural Networks (CNNs) have traditionally been hand-designed owing to the complexity of their construction and the computational requirements of their training. Recently however, there has been an increase in research interest towards automatically designing deep CNNs for specific tasks. Ensembling has been shown to effectively increase the performance of deep CNNs, although usually with a duplication of work and therefore a large increase in computational resources required. In this paper we present a method for automatically designing and ensembling deep CNN models with a central weight repository to avoid work duplication. The models are trained and optimised together using particle swarm optimisation (PSO), with architecture convergence encouraged. At the conclusion of the joint optimisation and training process a base model nomination method is used to determine the best candidates for the ensemble. Two base model nomination methods are proposed, one using the local best particle positions from the PSO process, and one using the contents of the central weight repository. Once the base model pool has been created, the individual models inherit their parameters from the central weight repository and are then finetuned and ensembled in order to create a final system. We evaluate our system on the CIFAR-10 classification dataset and demonstrate improved results over the single global best model suggested by the optimisation process, with a minor increase in resources required by the finetuning process. Our system achieves an error rate of 4.27% on the CIFAR-10 image classification task with only 36 hours of combined optimisation and training on a single NVIDIA GTX 1080Ti GPU
DEFEG: deep ensemble with weighted feature generation.
With the significant breakthrough of Deep Neural Networks in recent years, multi-layer architecture has influenced other sub-fields of machine learning including ensemble learning. In 2017, Zhou and Feng introduced a deep random forest called gcForest that involves several layers of Random Forest-based classifiers. Although gcForest has outperformed several benchmark algorithms on specific datasets in terms of classification accuracy and model complexity, its input features do not ensure better performance when going deeply through layer-by-layer architecture. We address this limitation by introducing a deep ensemble model with a novel feature generation module. Unlike gcForest where the original features are concatenated to the outputs of classifiers to generate the input features for the subsequent layer, we integrate weights on the classifiersâ outputs as augmented features to grow the deep model. The usage of weights in the feature generation process can adjust the input data of each layer, leading the better results for the deep model. We encode the weights using variable-length encoding and develop a variable-length Particle Swarm Optimisation method to search for the optimal values of the weights by maximizing the classification accuracy on the validation data. Experiments on a number of UCI datasets confirm the benefit of the proposed method compared to some well-known benchmark algorithms
Creative or Not? Birds and Ants Draw with Muscle
In this work, a novel approach of merging two swarm intelligence algorithms is considered â one mimicking the behaviour of ants foraging (Stochastic Diffusion Search [5]) and the other algorithm simulating the behaviour of birds flocking (Particle Swarm Optimisation [17]). This hybrid algorithm is assisted by a mechanism inspired from the behaviour of skeletal muscles activated by motor neurons. The operation of the swarm intelligence algorithms is first introduced via metaphor before the new hybrid algorithm is defined. Next, the novel behaviour of the hybrid algorithm is reflected through a cooperative attempt to make a drawing, followed by a discussion about creativity in general and the âcomputational creativityâ of the swarm
A survey on computational intelligence approaches for predictive modeling in prostate cancer
Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex forconventional statistical techniques to process quickly and eciently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known asmetaheuristic optimisation, nature inspired optimisation algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these,as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed
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