Sistemas granulares evolutivos

Orientador: Fernando Antonio Campos GomideTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Recentemente tem-se observado um crescente interesse em abordagens de modelagem computacional para lidar com fluxos de dados do mundo real. Métodos e algoritmos têm sido propostos para obtenção de conhecimento a partir de conjuntos de dados muito grandes e, a princípio, sem valor aparente. Este trabalho apresenta uma plataforma computacional para modelagem granular evolutiva de fluxos de dados incertos. Sistemas granulares evolutivos abrangem uma variedade de abordagens para modelagem on-line inspiradas na forma com que os humanos lidam com a complexidade. Esses sistemas exploram o fluxo de informação em ambiente dinâmico e extrai disso modelos que podem ser linguisticamente entendidos. Particularmente, a granulação da informação é uma técnica natural para dispensar atenção a detalhes desnecessários e enfatizar transparência, interpretabilidade e escalabilidade de sistemas de informação. Dados incertos (granulares) surgem a partir de percepções ou descrições imprecisas do valor de uma variável. De maneira geral, vários fatores podem afetar a escolha da representação dos dados tal que o objeto representativo reflita o significado do conceito que ele está sendo usado para representar. Neste trabalho são considerados dados numéricos, intervalares e fuzzy; e modelos intervalares, fuzzy e neuro-fuzzy. A aprendizagem de sistemas granulares é baseada em algoritmos incrementais que constroem a estrutura do modelo sem conhecimento anterior sobre o processo e adapta os parâmetros do modelo sempre que necessário. Este paradigma de aprendizagem é particularmente importante uma vez que ele evita a reconstrução e o retreinamento do modelo quando o ambiente muda. Exemplos de aplicação em classificação, aproximação de função, predição de séries temporais e controle usando dados sintéticos e reais ilustram a utilidade das abordagens de modelagem granular propostas. O comportamento de fluxos de dados não-estacionários com mudanças graduais e abruptas de regime é também analisado dentro do paradigma de computação granular evolutiva. Realçamos o papel da computação intervalar, fuzzy e neuro-fuzzy em processar dados incertos e prover soluções aproximadas de alta qualidade e sumário de regras de conjuntos de dados de entrada e saída. As abordagens e o paradigma introduzidos constituem uma extensão natural de sistemas inteligentes evolutivos para processamento de dados numéricos a sistemas granulares evolutivos para processamento de dados granularesAbstract: In recent years there has been increasing interest in computational modeling approaches to deal with real-world data streams. Methods and algorithms have been proposed to uncover meaningful knowledge from very large (often unbounded) data sets in principle with no apparent value. This thesis introduces a framework for evolving granular modeling of uncertain data streams. Evolving granular systems comprise an array of online modeling approaches inspired by the way in which humans deal with complexity. These systems explore the information flow in dynamic environments and derive from it models that can be linguistically understood. Particularly, information granulation is a natural technique to dispense unnecessary details and emphasize transparency, interpretability and scalability of information systems. Uncertain (granular) data arise from imprecise perception or description of the value of a variable. Broadly stated, various factors can affect one's choice of data representation such that the representing object conveys the meaning of the concept it is being used to represent. Of particular concern to this work are numerical, interval, and fuzzy types of granular data; and interval, fuzzy, and neurofuzzy modeling frameworks. Learning in evolving granular systems is based on incremental algorithms that build model structure from scratch on a per-sample basis and adapt model parameters whenever necessary. This learning paradigm is meaningful once it avoids redesigning and retraining models all along if the system changes. Application examples in classification, function approximation, time-series prediction and control using real and synthetic data illustrate the usefulness of the granular approaches and framework proposed. The behavior of nonstationary data streams with gradual and abrupt regime shifts is also analyzed in the realm of evolving granular computing. We shed light upon the role of interval, fuzzy, and neurofuzzy computing in processing uncertain data and providing high-quality approximate solutions and rule summary of input-output data sets. The approaches and framework introduced constitute a natural extension of evolving intelligent systems over numeric data streams to evolving granular systems over granular data streamsDoutoradoAutomaçãoDoutor em Engenharia Elétric

Long-term learning for type-2 neural-fuzzy systems

The development of a new long-term learning framework for interval-valued neural-fuzzy systems is presented for the first time in this article. The need for such a framework is twofold: to address continuous batch learning of data sets, and to take advantage the extra degree of freedom that type-2 Fuzzy Logic systems offer for better model predictive ability. The presented long-term learning framework uses principles of granular computing (GrC) to capture information/knowledge from raw data in the form of interval-valued sets in order to build a computational mechanism that has the ability to adapt to new information in an additive and long-term learning fashion. The latter, is to accommodate new input–output mappings and new classes of data without significantly disturbing existing input–output mappings, therefore maintaining existing performance while creating and integrating new knowledge (rules). This is achieved via an iterative algorithmic process, which involves a two-step operation: iterative rule-base growth (capturing new knowledge) and iterative rule-base pruning (removing redundant knowledge) for type-2 rules. The two-step operation helps create a growing, but sustainable model structure. The performance of the proposed system is demonstrated using a number of well-known non-linear benchmark functions as well as a highly nonlinear multivariate real industrial case study. Simulation results show that the performance of the original model structure is maintained and it is comparable to the updated model's performance following the incremental learning routine. The study is concluded by evaluating the performance of the proposed framework in frequent and consecutive model updates where the balance between model accuracy and complexity is further assessed

Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets

In many real application areas, the data used are highly skewed and the number of instances for some classes are much higher than that of the other classes. Solving a classification task using such an imbalanced data-set is difficult due to the bias of the training towards the majority classes. The aim of this paper is to improve the performance of fuzzy rule based classification systems on imbalanced domains, increasing the granularity of the fuzzy partitions on the boundary areas between the classes, in order to obtain a better separability. We propose the use of a hierarchical fuzzy rule based classification system, which is based on the refinement of a simple linguistic fuzzy model by means of the extension of the structure of the knowledge base in a hierarchical way and the use of a genetic rule selection process in order to get a compact and accurate model. The good performance of this approach is shown through an extensive experimental study carried out over a large collection of imbalanced data-sets.Spanish Ministry of Education and Science (MEC) under Projects TIN-2005-08386-C05-01 and TIN-2005-08386- C05-0

Context dependent fuzzy modelling and its applications

Fuzzy rule-based systems (FRBS) use the principle of fuzzy sets and fuzzy logic to describe vague and imprecise statements and provide a facility to express the behaviours of the system with a human-understandable language. Fuzzy information, once defined by a fuzzy system, is fixed regardless of the circumstances and therefore makes it very difficult to capture the effect of context on the meaning of the fuzzy terms. While efforts have been made to integrate contextual information into the representation of fuzzy sets, it remains the case that often the context model is very restrictive and/or problem specific. The work reported in this thesis is our attempt to create a practical frame work to integrate contextual information into the representation of fuzzy sets so as to improve the interpretability as well as the accuracy of the fuzzy system. Throughout this thesis, we have looked at the capability of the proposed context dependent fuzzy sets as a stand alone as well as in combination with other methods in various application scenarios ranging from time series forecasting to complicated car racing control systems. In all of the applications, the highly competitive performance nature of our approach has proven its effectiveness and efficiency compared with existing techniques in the literature

Context dependent fuzzy modelling and its applications

Fuzzy rule-based systems (FRBS) use the principle of fuzzy sets and fuzzy logic to describe vague and imprecise statements and provide a facility to express the behaviours of the system with a human-understandable language. Fuzzy information, once defined by a fuzzy system, is fixed regardless of the circumstances and therefore makes it very difficult to capture the effect of context on the meaning of the fuzzy terms. While efforts have been made to integrate contextual information into the representation of fuzzy sets, it remains the case that often the context model is very restrictive and/or problem specific. The work reported in this thesis is our attempt to create a practical frame work to integrate contextual information into the representation of fuzzy sets so as to improve the interpretability as well as the accuracy of the fuzzy system. Throughout this thesis, we have looked at the capability of the proposed context dependent fuzzy sets as a stand alone as well as in combination with other methods in various application scenarios ranging from time series forecasting to complicated car racing control systems. In all of the applications, the highly competitive performance nature of our approach has proven its effectiveness and efficiency compared with existing techniques in the literature

A fuzzy approach to text classification with two-stage training for ambiguous instances

Sentiment analysis is a very popular application area of text mining and machine learning. The popular methods include Support Vector Machine, Naive Bayes, Decision Trees and Deep Neural Networks. However, these methods generally belong to discriminative learning, which aims to distinguish one class from others with a clear-cut outcome, under the presence of ground truth. In the context of text classification, instances are naturally fuzzy (can be multi-labeled in some application areas) and thus are not considered clear-cut, especially given the fact that labels assigned to sentiment in text represent an agreed level of subjective opinion for multiple human annotators rather than indisputable ground truth. This has motivated researchers to develop fuzzy methods, which typically train classifiers through generative learning, i.e. a fuzzy classifier is used to measure the degree to which an instance belongs to each class. Traditional fuzzy methods typically involve generation of a single fuzzy classifier and employ a fixed rule of defuzzification outputting the class with the maximum membership degree. The use of a single fuzzy classifier with the above fixed rule of defuzzification is likely to get the classifier encountering the text ambiguity situation on sentiment data, i.e. an instance may obtain equal membership degrees to both the positive and negative classes. In this paper, we focus on cyberhate classification, since the spread of hate speech via social media can have disruptive impacts on social cohesion and lead to regional and community tensions. Automatic detection of cyberhate has thus become a priority research area. In particular, we propose a modified fuzzy approach with two stage training for dealing with text ambiguity and classifying four types of hate speech, namely: religion, race, disability and sexual orientation - and compare its performance with those popular methods as well as some existing fuzzy approaches, while the features are prepared through the Bag-of-Words and Word Embedding feature extraction methods alongside the correlation based feature subset selection method. The experimental results show that the proposed fuzzy method outperforms the other methods in most cases

Risk prediction analysis for post-surgical complications in cardiothoracic surgery

Cardiothoracic surgery patients have the risk of developing surgical site infections (SSIs), which causes hospital readmissions, increases healthcare costs and may lead to mortality. The first 30 days after hospital discharge are crucial for preventing these kind of infections. As an alternative to a hospital-based diagnosis, an automatic digital monitoring system can help with the early detection of SSIs by analyzing daily images of patient’s wounds. However, analyzing a wound automatically is one of the biggest challenges in medical image analysis. The proposed system is integrated into a research project called CardioFollowAI, which developed a digital telemonitoring service to follow-up the recovery of cardiothoracic surgery patients. This present work aims to tackle the problem of SSIs by predicting the existence of worrying alterations in wound images taken by patients, with the help of machine learning and deep learning algorithms. The developed system is divided into a segmentation model which detects the wound region area and categorizes the wound type, and a classification model which predicts the occurrence of alterations in the wounds. The dataset consists of 1337 images with chest wounds (WC), drainage wounds (WD) and leg wounds (WL) from 34 cardiothoracic surgery patients. For segmenting the images, an architecture with a Mobilenet encoder and an Unet decoder was used to obtain the regions of interest (ROI) and attribute the wound class. The following model was divided into three sub-classifiers for each wound type, in order to improve the model’s performance. Color and textural features were extracted from the wound’s ROIs to feed one of the three machine learning classifiers (random Forest, support vector machine and K-nearest neighbors), that predict the final output. The segmentation model achieved a final mean IoU of 89.9%, a dice coefficient of 94.6% and a mean average precision of 90.1%, showing good results. As for the algorithms that performed classification, the WL classifier exhibited the best results with a 87.6% recall and 52.6% precision, while WC classifier achieved a 71.4% recall and 36.0% precision. The WD had the worst performance with a 68.4% recall and 33.2% precision. The obtained results demonstrate the feasibility of this solution, which can be a start for preventing SSIs through image analysis with artificial intelligence.Os pacientes submetidos a uma cirurgia cardiotorácica tem o risco de desenvolver infeções no local da ferida cirúrgica, o que pode consequentemente levar a readmissões hospitalares, ao aumento dos custos na saúde e à mortalidade. Os primeiros 30 dias após a alta hospitalar são cruciais na prevenção destas infecções. Assim, como alternativa ao diagnóstico no hospital, a utilização diária de um sistema digital e automático de monotorização em imagens de feridas cirúrgicas pode ajudar na precoce deteção destas infeções. No entanto, a análise automática de feridas é um dos grandes desafios em análise de imagens médicas. O sistema proposto integra um projeto de investigação designado CardioFollow.AI, que desenvolveu um serviço digital de telemonitorização para realizar o follow-up da recuperação dos pacientes de cirurgia cardiotorácica. Neste trabalho, o problema da infeção de feridas cirúrgicas é abordado, através da deteção de alterações preocupantes na ferida com ajuda de algoritmos de aprendizagem automática. O sistema desenvolvido divide-se num modelo de segmentação, que deteta a região da ferida e a categoriza consoante o seu tipo, e num modelo de classificação que prevê a existência de alterações na ferida. O conjunto de dados consistiu em 1337 imagens de feridas do peito (WC), feridas dos tubos de drenagem (WD) e feridas da perna (WL), provenientes de 34 pacientes de cirurgia cardiotorácica. A segmentação de imagem foi realizada através da combinação de Mobilenet como codificador e Unet como decodificador, de forma a obter-se as regiões de interesse e atribuir a classe da ferida. O modelo seguinte foi dividido em três subclassificadores para cada tipo de ferida, de forma a melhorar a performance do modelo. Caraterísticas de cor e textura foram extraídas da região da ferida para serem introduzidas num dos modelos de aprendizagem automática de forma a prever a classificação final (Random Forest, Support Vector Machine and K-Nearest Neighbors). O modelo de segmentação demonstrou bons resultados ao obter um IoU médio final de 89.9%, um dice de 94.6% e uma média de precisão de 90.1%. Relativamente aos algoritmos que realizaram a classificação, o classificador WL exibiu os melhores resultados com 87.6% de recall e 62.6% de precisão, enquanto o classificador das WC conseguiu um recall de 71.4% e 36.0% de precisão. Por fim, o classificador das WD teve a pior performance com um recall de 68.4% e 33.2% de precisão. Os resultados obtidos demonstram a viabilidade desta solução, que constitui o início da prevenção de infeções em feridas cirúrgica a partir da análise de imagem, com recurso a inteligência artificial

Z-Numbers-Based Approach to Hotel Service Quality Assessment

In this study, we are analyzing the possibility of using Z-numbers for measuring the service quality and decision-making for quality improvement in the hotel industry. Techniques used for these purposes are based on consumer evalu- ations - expectations and perceptions. As a rule, these evaluations are expressed in crisp numbers (Likert scale) or fuzzy estimates. However, descriptions of the respondent opinions based on crisp or fuzzy numbers formalism not in all cases are relevant. The existing methods do not take into account the degree of con- fidence of respondents in their assessments. A fuzzy approach better describes the uncertainties associated with human perceptions and expectations. Linguis- tic values are more acceptable than crisp numbers. To consider the subjective natures of both service quality estimates and confidence degree in them, the two- component Z-numbers Z = (A, B) were used. Z-numbers express more adequately the opinion of consumers. The proposed and computationally efficient approach (Z-SERVQUAL, Z-IPA) allows to determine the quality of services and iden- tify the factors that required improvement and the areas for further development. The suggested method was applied to evaluate the service quality in small and medium-sized hotels in Turkey and Azerbaijan, illustrated by the example

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Contains fulltext : 228326pre.pdf (preprint version ) (Open Access) Contains fulltext : 228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202