An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

Existing FNNs are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be automatically extracted from data streams or removed if they play limited role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely gClass, drives the hidden layer. It is equipped by an automatic feature selection method which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of dimensionality of input space due to the nature of feature augmentation approach in building a deep network structure. DEVFNN works in the sample-wise fashion and is compatible for data stream applications. The efficacy of DEVFNN has been thoroughly evaluated using seven datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four popular continual learning algorithms and its shallow counterpart where DEVFNN demonstrates improvement of classification accuracy. Moreover, it is also shown that the concept drift detection method is an effective tool to control the depth of network structure while the hidden layer merging scenario is capable of simplifying the network complexity of a deep network with negligible compromise of generalization performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System

On the Differential Analysis of Enterprise Valuation Methods as a Guideline for Unlisted Companies Assessment (II): Applying Machine-Learning Techniques for Unbiased Enterprise Value Assessment

The search for an unbiased company valuation method to reduce uncertainty, whether or not it is automatic, has been a relevant topic in social sciences and business development for decades. Many methods have been described in the literature, but consensus has not been reached. In the companion paper we aimed to review the assessment capabilities of traditional company valuation model, based on company’s intrinsic value using the Discounted Cash Flow (DCF). In this paper, we capitalized on the potential of exogenous information combined with Machine Learning (ML) techniques. To do so, we performed an extensive analysis to evaluate the predictive capabilities with up to 18 different ML techniques. Endogenous variables (features) related to value creation (DCF) were proved to be crucial elements for the models, while the incorporation of exogenous, industry/country specific ones, incrementally improves the ML performance. Bagging Trees, Supported Vector Machine Regression, Gaussian Process Regression methods consistently provided the best results. We concluded that an unbiased model can be created based on endogenous and exogenous information to build a reference framework, to price and benchmark Enterprise Value for valuation and credit risk assessment

Deep Stacked Stochastic Configuration Networks for Lifelong Learning of Non-Stationary Data Streams

The concept of SCN offers a fast framework with universal approximation guarantee for lifelong learning of non-stationary data streams. Its adaptive scope selection property enables for proper random generation of hidden unit parameters advancing conventional randomized approaches constrained with a fixed scope of random parameters. This paper proposes deep stacked stochastic configuration network (DSSCN) for continual learning of non-stationary data streams which contributes two major aspects: 1) DSSCN features a self-constructing methodology of deep stacked network structure where hidden unit and hidden layer are extracted automatically from continuously generated data streams; 2) the concept of SCN is developed to randomly assign inverse covariance matrix of multivariate Gaussian function in the hidden node addition step bypassing its computationally prohibitive tuning phase. Numerical evaluation and comparison with prominent data stream algorithms under two procedures: periodic hold-out and prequential test-then-train processes demonstrate the advantage of proposed methodology.Comment: This paper has been published in Information Science

Ensemble Methods for Consumer Price Inflation Forecasting

Inflation forecasting is one of the central issues in micro and macroeconomics. Standard forecasting methods tend to follow a winner-take-all approach by which, for each time series, a single believed to be the best method is chosen from a pool of competing models. This paper investigates the predictive accuracy of a metalearning strategy called Arbitrated Dynamic Ensemble (ADE) in inflation forecasting using United States data. The findings show that: i) the SARIMA model exhibits the best average rank relative to ADE and competing state-of-the-art model combination and metalearning methods; ii) the ADE methodology presents a better average rank compared to widely used model combination approaches, including the original Arbitrating approach, Stacking, Simple averaging, Fixed Share, or weighted adaptive combination of experts; iii) the ADE approach benefits from combining the base-learners as opposed to selecting the best forecasting model or using all experts; iv) the method is sensitive to the aggregation (weighting) mechanism

An Evaluation of the Use of Diversity to Improve the Accuracy of Predicted Ratings in Recommender Systems

The diversity; versus accuracy trade off, has become an important area of research within recommender systems as online retailers attempt to better serve their customers and gain a competitive advantage through an improved customer experience. This dissertation attempted to evaluate the use of diversity measures in predictive models as a means of improving predicted ratings. Research literature outlines a number of influencing factors such as personality, taste, mood and social networks in addition to approaches to the diversity challenge post recommendation. A number of models were applied included DecisionStump, Linear Regression, J48 Decision Tree and Naive Bayes. Various evaluation metrics such as precision, recall, ROC area, mean squared error and correlation coefficient were used to evaluate the model types. The results were below a benchmark selected during the literature review. The experiment did not demonstrate that diversity measures as inputs improve the accuracy of predicted ratings. However, the evaluation results for the model without diversity measures were low also and comparable to those with diversity indicating that further research in this area may be worthwhile. While the experiment conducted did not clearly demonstrate that the inclusion of diversity measures as inputs improve the accuracy of predicted ratings, approaches to data extraction, pre-processing, and model selection could inform further research. Areas of further research identified within this paper may also add value for those interested in this topic

Contributions to comprehensible classification

xxx, 240 p.La tesis doctoral descrita en esta memoria ha contribuido a la mejora de dos tipos de algoritmos declasificación comprensibles: algoritmos de \'arboles de decisión consolidados y algoritmos de inducciónde reglas tipo PART.En cuanto a las contribuciones a la consolidación de algoritmos de árboles de decisión, se hapropuesto una nueva estrategia de remuestreo que ajusta el número de submuestras para permitir cambiarla distribución de clases en las submuestras sin perder información. Utilizando esta estrategia, la versiónconsolidada de C4.5 (CTC) obtiene mejores resultados que un amplio conjunto de algoritmoscomprensibles basados en algoritmos genéticos y clásicos. Tres nuevos algoritmos han sido consolidados:una variante de CHAID (CHAID*) y las versiones Probability Estimation Tree de C4.5 y CHAID* (C4.4y CHAIC). Todos los algoritmos consolidados obtienen mejores resultados que sus algoritmos de\'arboles de decisión base, con tres algoritmos consolidados clasificándose entre los cuatro mejores en unacomparativa. Finalmente, se ha analizado el efecto de la poda en algoritmos simples y consolidados de\'arboles de decisión, y se ha concluido que la estrategia de poda propuesta en esta tesis es la que obtiene mejores resultados.En cuanto a las contribuciones a algoritmos tipo PART de inducción de reglas, una primerapropuesta cambia varios aspectos de como PART genera \'arboles parciales y extrae reglas de estos, locual resulta en clasificadores con mejor capacidad de generalizar y menor complejidad estructuralcomparando con los generados por PART. Una segunda propuesta utiliza \'arboles completamentedesarrollados, en vez de parcialmente desarrollados, y genera conjuntos de reglas que obtienen aúnmejores resultados de clasificación y una complejidad estructural menor. Estas dos nuevas propuestas y elalgoritmo PART original han sido complementadas con variantes basadas en CHAID* para observar siestos beneficios pueden ser trasladados a otros algoritmos de \'arboles de decisión y se ha observado, dehecho, que los algoritmos tipo PART basados en CHAID* también crean clasificadores más simples ycon mejor capacidad de clasificar que CHAID

Enhanced Prediction of Network Attacks Using Incomplete Data

For years, intrusion detection has been considered a key component of many organizations’ network defense capabilities. Although a number of approaches to intrusion detection have been tried, few have been capable of providing security personnel responsible for the protection of a network with sufficient information to make adjustments and respond to attacks in real-time. Because intrusion detection systems rarely have complete information, false negatives and false positives are extremely common, and thus valuable resources are wasted responding to irrelevant events. In order to provide better actionable information for security personnel, a mechanism for quantifying the confidence level in predictions is needed. This work presents an approach which seeks to combine a primary prediction model with a novel secondary confidence level model which provides a measurement of the confidence in a given attack prediction being made. The ability to accurately identify an attack and quantify the confidence level in the prediction could serve as the basis for a new generation of intrusion detection devices, devices that provide earlier and better alerts for administrators and allow more proactive response to events as they are occurring

Contributions to comprehensible classification

xxx, 240 p.La tesis doctoral descrita en esta memoria ha contribuido a la mejora de dos tipos de algoritmos declasificación comprensibles: algoritmos de \'arboles de decisión consolidados y algoritmos de inducciónde reglas tipo PART.En cuanto a las contribuciones a la consolidación de algoritmos de árboles de decisión, se hapropuesto una nueva estrategia de remuestreo que ajusta el número de submuestras para permitir cambiarla distribución de clases en las submuestras sin perder información. Utilizando esta estrategia, la versiónconsolidada de C4.5 (CTC) obtiene mejores resultados que un amplio conjunto de algoritmoscomprensibles basados en algoritmos genéticos y clásicos. Tres nuevos algoritmos han sido consolidados:una variante de CHAID (CHAID*) y las versiones Probability Estimation Tree de C4.5 y CHAID* (C4.4y CHAIC). Todos los algoritmos consolidados obtienen mejores resultados que sus algoritmos de\'arboles de decisión base, con tres algoritmos consolidados clasificándose entre los cuatro mejores en unacomparativa. Finalmente, se ha analizado el efecto de la poda en algoritmos simples y consolidados de\'arboles de decisión, y se ha concluido que la estrategia de poda propuesta en esta tesis es la que obtiene mejores resultados.En cuanto a las contribuciones a algoritmos tipo PART de inducción de reglas, una primerapropuesta cambia varios aspectos de como PART genera \'arboles parciales y extrae reglas de estos, locual resulta en clasificadores con mejor capacidad de generalizar y menor complejidad estructuralcomparando con los generados por PART. Una segunda propuesta utiliza \'arboles completamentedesarrollados, en vez de parcialmente desarrollados, y genera conjuntos de reglas que obtienen aúnmejores resultados de clasificación y una complejidad estructural menor. Estas dos nuevas propuestas y elalgoritmo PART original han sido complementadas con variantes basadas en CHAID* para observar siestos beneficios pueden ser trasladados a otros algoritmos de \'arboles de decisión y se ha observado, dehecho, que los algoritmos tipo PART basados en CHAID* también crean clasificadores más simples ycon mejor capacidad de clasificar que CHAID

Neuroevolutionary learning in nonstationary environments

This work presents a new neuro-evolutionary model, called NEVE (Neuroevolutionary Ensemble), based on an ensemble of Multi-Layer Perceptron (MLP) neural networks for learning in nonstationary environments. NEVE makes use of quantum-inspired evolutionary models to automatically configure the ensemble members and combine their output. The quantum-inspired evolutionary models identify the most appropriate topology for each MLP network, select the most relevant input variables, determine the neural network weights and calculate the voting weight of each ensemble member. Four different approaches of NEVE are developed, varying the mechanism for detecting and treating concepts drifts, including proactive drift detection approaches. The proposed models were evaluated in real and artificial datasets, comparing the results obtained with other consolidated models in the literature. The results show that the accuracy of NEVE is higher in most cases and the best configurations are obtained using some mechanism for drift detection. These results reinforce that the neuroevolutionary ensemble approach is a robust choice for situations in which the datasets are subject to sudden changes in behaviour