Objetivo general

Figure 9.1: The EDBN Architecture Providing Computational Intelligent Systems Solutions to Address DBN Modelling Issues.

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It is common knowledge and inevitable that at the initial stages when a new technology is introduced, it may be less welcomed by the public than a few years down the line when it is better known, and has been improved upon. One would recall that the architecture initially proposed for the aeroplane in some decades back was not expected by the public to be as good as what it is producing today. In the same way, the architecture proposed in this thesis will potentially one day provide most computational intelligence solutions to many issues in the areas of science and technology. This chapter therefore summarises this research and the contributions of the proposed newly developed EDBN (Evolving Dynamic Bayesian Network) architecture to mitigate issues with DBN (Dynamic Bayesian Network) models as shown in Figure 9.1. It is worth noting that part of the originalities of our approach includes illustrations and the semantics one can associate with them.

Chapter Two critically reviewed what researchers have done so far in DBN models, described why Bayesian learning research cannot be separated from DBNs, and presented the suggested solutions for addressing the ongoing challenges of learning. Our analysis, illustrative descriptions and comparisons revealed that most of the existing DBN models, such as the Factorial HMM, Coupled HMM, Autoregressive HMM, PDBN, etc. are based on HMM representations, which have contributed to the baseline of temporal modelling; however, they are still rigid and are limited in their expressive power.

The representations of these models are often associated with skilled users or domain experts, which means that non-skilled users struggle to use them. The full capabilities of DBNs have not been exploited and learning such models from massive datasets requires an economic solution that is completely scalable. These significant reviews contributed to applications in [51] [73].

In Chapter Three, theoretical backgrounds of DBNs are methodically described, substantiating the development of the proposed EDBN architecture. It addresses and accounts for the inherent ongoing computational intensities associated with learning BNs (Bayesian Networks) from massive datasets. The focus of Chapter Three is on providing fundamental theories such as principles of probability, situation awareness (SA) and calculus, which can be used to easily evolve BNs dynamically. These theoretical backgrounds contributed to applications in [69] [89].

After our preliminary research investigations into various Bayesian learning algorithms, we first identified ‘backtracking’ as an overhead of the computational intensity issues presented in Chapter Four.

In that chapter, we presented an improved GA (genetic algorithm) to emerge optimal Bayesian Networks with the emphasis on avoiding backtracking problems and integration of the ADTree technique. The GA was benchmarked with some known networks to show that it can structurally discover equivalent network models and we compared its performance with the Weka system, the Hugin system and the work of William et al. [39]. It is one of the major components of the EDBN. The chapter therefore demonstrated how immediate detection of telecommunication anomalies can be achieved, as optimized learning

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algorithms like the GA can emerge individual Bayesian Networks to enhance the response rates of detection models. The enhanced GA and its applications to achieve immediate detections in telecommunications anomaly led to publications in [51] [63].

Varieties of existing DBN models are restricted (or rigid) with assumptions and are limited in their expressive power because they often require the intervention of domain experts. After extensive research investigations, we first clarified the models into three well-defined research niche areas and introduced the EDBN architecture to address the third research niche area without technicalities so as to accommodate all readers. Chapter Five integrates two new orthogonal frameworks as the development of the EDBN architecture for temporal probabilistic modelling from MTS and as an alternative economic solution to the computational intensity (or NP-hard) problems arising in intelligent systems. The temporal probabilistic modelling framework derived the ESA and EFSA technologies, where the ESA monitors complex environments over current time steps and the EFSA projects the behaviour of the environments into the future. The DMMAL (dynamic memory management) framework provides a scalable economic solution for handling massive datasets and models. From the evaluations, the chapter reveals that the EDBN has the potential to become a more powerful deliberative and reactive architecture that finally puts an end to the worries of non-expert practitioners with regard to choosing the appropriate model among DBN types; it also ends the computational problems raised in various research efforts. This contributed to the publications in [68] [71].

Decision-makers such as non-expert practitioners struggle to interpret most existing DBN models when attempting to take correct actions. This has consequently led to ongoing information gap problems (e.g. between complex models and correct interpretation by decision-makers) [16]. The information gap problem is a result of decision-makers not being well acquainted with being patterns currently occurring in their various domains. Bridging this gap is a challenge. Our ESA technology simply enables one to have better understanding over time about current hidden situational patterns embedded in any complex systems of interest. It is rigorously subjected to a number of evaluations to show that it can reveal variability for having a detailed pattern understanding of real-life problems. Its power motivates the successful applications of the ESA to many areas, most notably in the assessments of rainfall onsets for southern Africa using real-life data from a meteorological station [70], in business intelligence research of the southern Africa Institute for Management Scientists [68], in water quality management [85] and in project profitability analysis [69].

Prediction into the future for making anticipatory planning is often said to be the cornerstone of every successful business. The varieties of the existing DBNs obviously opened up challenging problems to non-expert practitioners such as managers and decision makers on how to choose the most suitable model for various real-life applications. Some prediction models such as statistical models and HMMs

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also suffer from convergence or exponential problems over wider time steps [8]. That is, the prediction steps get stuck towards zero or tend towards infinity. The EFSA technology takes the ESA further by reasoning and projecting anticipatory situations into the future. The EFSA eliminates the worries of choosing a good DBN model type, with its automatic and complete emergence of temporal models over time from historical MTS. The EFSA’s modelling spans orthogonal two-dimensional (2D) time space as a prediction strategy to avoid convergence problems. It has been rigorously subjected to a number of prediction evaluations using publicly available MTS in the UCI repository and real-life datasets obtained locally, which led to publications in [71]. The experimental studies show that the EFSA can potentially one day become a powerful temporal probabilistic modelling approach for solving most anticipatory problems in science and industry.

One can recall that researchers and practitioners have often stressed that learning Bayesian networks from massive datasets is a computationally intensive problem. Yet, very little research shows how popular single-user machines (e.g. desktops, laptops, etc.) can find solutions to learn Bayesian networks from massive datasets, despite the fact that there are methods of distributed learning on networks, which are, however, expensive to set up. With further investigations into the computational intensity problems that may arise from the DBNs, the economic scalable framework of the EDBN that we propose is reactive in nature and is called the DMMAL (Dynamic Memory Management in Adaptive Learning). Using various components such as the discretizer actuators, RDP (representative data partitioning) algorithm and the CPT actuators, DMMAL optimizes computational time efficiency and saves massive Bayesian Network models from crashing, even when used on reasonably inexpensive single-user hardware, such as a PC or laptop. One of its greatest competitive advantages, especially in developing countries, is the capability of DMMAL to continue modelling where execution stops, if the learning process is accidentally suspended, possibly due to electricity power failure. The excellent results obtained motivate its application in [73] [89].

The new class of temporal probabilistic modelling using the EDBN architecture presented in this thesis has great universality of applications to wider real-life areas, with the added benefit of being able to accommodate all users. We relaxed the limited expressive power on the previous DBN work by completely emerging the network structures and the CPTs of DBN models with integration of scalability.

This has been avoided by existing DBN modelling approaches for convenient modelling. Thus, the impact of effective monitoring and good planning is often felt in environments using sophisticated computational intelligent systems, such as the EDBN.

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