553 research outputs found
Combining Classification and Clustering for Tweet Sentiment Analysis
The goal of sentiment analysis is to determine opinions, emotions, and attitudes presented in source material. In tweet sentiment analysis, opinions in messages can be typically categorized as positive or negative. To classify them, researchers have been using traditional classifiers like Naive Bayes, Maximum Entropy, and Support Vector Machines (SVM). In this paper, we show that a SVM classifier combined with a cluster ensemble can offer better classification accuracies than a stand-alone SVM. In our study, we employed an algorithm, named 'C POT.3'E-SL, capable to combine classifier and cluster ensembles. This algorithm can refine tweet classifications from additional information provided by clusterers, assuming that similar instances from the same clusters are more likely to share the same class label. The resulting classifier has shown to be competitive with the best results found so far in the literature, thereby suggesting that the studied approach is promising for tweet sentiment classification.Capes (Proc. DS-7253238/D)CNPq (Proc. 303348/2013-5)FAPESP (Proc. 2013/07375-0 and 2010/20830-0
Weed/Plant Classification Using Evolutionary Optimised Ensemble Based On Local Binary Patterns
This thesis presents a novel pixel-level weed classification through rotation-invariant uniform local binary pattern (LBP) features for precision weed control. Based on two-level optimisation structure; First, Genetic Algorithm (GA) optimisation to select the best rotation-invariant uniform LBP configurations; Second, Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in the Neural Network (NN) ensemble to select the best combinations of voting weights of the predicted outcome for each classifier. The model obtained 87.9% accuracy in CWFID public benchmark
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends
Computer vision (CV) is a big and important field
in artificial intelligence covering a wide range of applications.
Image analysis is a major task in CV aiming to extract, analyse
and understand the visual content of images. However, imagerelated
tasks are very challenging due to many factors, e.g., high
variations across images, high dimensionality, domain expertise
requirement, and image distortions. Evolutionary computation
(EC) approaches have been widely used for image analysis with
significant achievement. However, there is no comprehensive
survey of existing EC approaches to image analysis. To fill
this gap, this paper provides a comprehensive survey covering
all essential EC approaches to important image analysis tasks
including edge detection, image segmentation, image feature
analysis, image classification, object detection, and others. This
survey aims to provide a better understanding of evolutionary
computer vision (ECV) by discussing the contributions of different
approaches and exploring how and why EC is used for
CV and image analysis. The applications, challenges, issues, and
trends associated to this research field are also discussed and
summarised to provide further guidelines and opportunities for
future research
A Multi-Population FA for Automatic Facial Emotion Recognition
Automatic facial emotion recognition system is popular in various domains such as health care, surveillance and human-robot interaction. In this paper we present a novel multi-population FA for automatic facial emotion recognition. The overall system is equipped with horizontal vertical neighborhood local binary patterns (hvnLBP) for feature extraction, a novel multi-population FA for feature selection and diverse classifiers for emotion recognition. First, we extract features using hvnLBP, which are robust to illumination changes, scaling and rotation variations. Then, a novel FA variant is proposed to further select most important and emotion specific features. These selected features are used as input to the classifier to further classify seven basic emotions. The proposed system is evaluated with multiple facial expression datasets and also compared with other state-of-the-art models
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML
Automated machine learning (AutoML) systems commonly ensemble models post hoc
to improve predictive performance, typically via greedy ensemble selection
(GES). However, we believe that GES may not always be optimal, as it performs a
simple deterministic greedy search. In this work, we introduce two novel
population-based ensemble selection methods, QO-ES and QDO-ES, and compare them
to GES. While QO-ES optimises solely for predictive performance, QDO-ES also
considers the diversity of ensembles within the population, maintaining a
diverse set of well-performing ensembles during optimisation based on ideas of
quality diversity optimisation. The methods are evaluated using 71
classification datasets from the AutoML benchmark, demonstrating that QO-ES and
QDO-ES often outrank GES, albeit only statistically significant on validation
data. Our results further suggest that diversity can be beneficial for post hoc
ensembling but also increases the risk of overfitting.Comment: 10 pages main paper, 24 pages references and appendix, 4 figures, 16
subfigures, 13 tables, to be published in: International Conference on
Automated Machine Learning 2023; affiliations corrected. arXiv admin note:
text overlap with arXiv:2307.0028
Risk prediction of product-harm events using rough sets and multiple classifier fusion:an experimental study of listed companies in China
With the increasing of frequency and destructiveness of product-harm events, study on enterprise crisis management becomes essentially important, but little literature thoroughly explores the risk prediction method of product-harm event. In this study, an initial index system for risk prediction was built based on the analysis of the key drivers of the product-harm event's evolution; ultimately, nine risk-forecasting indexes were obtained using rough set attribute reduction. With the four indexes of cumulative abnormal returns as the input, fuzzy clustering was used to classify the risk level of a product-harm event into four grades. In order to control the uncertainty and instability of single classifiers in risk prediction, multiple classifier fusion was introduced and combined with self-organising data mining (SODM). Further, an SODM-based multiple classifier fusion (SB-MCF) model was presented for the risk prediction related to a product-harm event. The experimental results based on 165 Chinese listed companies indicated that the SB-MCF model improved the average predictive accuracy and reduced variation degree simultaneously. The statistical analysis demonstrated that the SB-MCF model significantly outperformed six widely used single classification models (e.g. neural networks, support vector machine, and case-based reasoning) and other six commonly used multiple classifier fusion methods (e.g. majority voting, Bayesian method, and genetic algorithm)
Individual and ensemble functional link neural networks for data classification
This study investigated the Functional Link Neural Network (FLNN) for solving data classification problems. FLNN based models were developed using evolutionary methods as well as ensemble methods. The outcomes of the experiments covering benchmark classification problems, positively demonstrated the efficacy of the proposed models for undertaking data classification problems
Adaptive Algorithms For Classification On High-Frequency Data Streams: Application To Finance
MenciĂłn Internacional en el tĂtulo de doctorIn recent years, the problem of concept drift has gained importance in the financial
domain. The succession of manias, panics and crashes have stressed the nonstationary
nature and the likelihood of drastic structural changes in financial markets.
The most recent literature suggests the use of conventional machine learning and statistical
approaches for this. However, these techniques are unable or slow to adapt
to non-stationarities and may require re-training over time, which is computationally
expensive and brings financial risks.
This thesis proposes a set of adaptive algorithms to deal with high-frequency data
streams and applies these to the financial domain. We present approaches to handle
different types of concept drifts and perform predictions using up-to-date models.
These mechanisms are designed to provide fast reaction times and are thus applicable
to high-frequency data. The core experiments of this thesis are based on the prediction
of the price movement direction at different intraday resolutions in the SPDR S&P 500
exchange-traded fund. The proposed algorithms are benchmarked against other popular
methods from the data stream mining literature and achieve competitive results.
We believe that this thesis opens good research prospects for financial forecasting
during market instability and structural breaks. Results have shown that our proposed
methods can improve prediction accuracy in many of these scenarios. Indeed, the
results obtained are compatible with ideas against the efficient market hypothesis.
However, we cannot claim that we can beat consistently buy and hold; therefore, we
cannot reject it.Programa de Doctorado en Ciencia y TecnologĂa Informática por la Universidad Carlos III de MadridPresidente: Gustavo Recio Isasi.- Secretario: Pedro Isasi Viñuela.- Vocal: Sandra GarcĂa RodrĂgue
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