Advanced Methods for Loss Given Default Estimation

This thesis consists of three essays on the estimation of the risk parameter LGD. The first essay (Hartmann-Wendels, Miller, and Töws, 2014, Loss given default for leasing: Parametric and nonparametric estimations) fills a gap in LGD related literature by focusing on elementary differences of the examined estimation approaches. We find that finite mixture models are quite capable of reproducing the unusual shape of the LGD distribution. However, out-of-sample the estimation error increases significantly. Model trees produce robust in-sample and out-of-sample estimations. Furthermore, we find that the improvement of advanced models increases with an increasing dataset. The second essay (Töws, 2014, The impact of debtor recovery on loss given default) addresses the economic consideration of the workout process of defaulted contracts. Dependent on the lessor’s workout strategy, defaulted contracts may develop in two distinct ways. Either the default reason can be dissolved and the debtor recovers or the contract must be written off. The study indicates the benefits of establishing the lessor’s expertise in assessing a defaulted contract’s continuation worthiness. If successfully implemented, the resulting workout process should produce lower LGDs than before and thereby strengthen the lenders competitiveness. The third essay (Miller and Töws, 2015, Loss given default-adjusted workout processes for leases) contributes to the LGD estimation literature considering unique features of leasing contracts. We economically account for leasing peculiarities and develop a particularly suited approach to estimate the leasing contracts’ LGD. We separate the LGD into its cash flows of the workout process of defaulted leasing contracts. We find that the estimated values of the parts of the LGD are sturdy indicators for the success of the workout process. Thus, the lessor can benefit from the consideration of both these forecasts for his actions concerning the workout process

A review on Machine Learning Techniques

Machine learning is the essence of artificial intelligence. Machine Learning learns from past experiences to improve the performances of intelligent programs. Machine learning system builds the learning model that effectively “learns” how to estimate from training data of given example. IT refers to a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. In this new era, Machine learning is mostly in use to demonstrate the promise of producing consistently accurate estimates. The main goal and contribution of this review paper is to present the overview of machine learning and provides machine-learning techniques. Also paper reviews the merits and demerits of various machine learning algorithms in different approaches

Learning from accidents : machine learning for safety at railway stations

In railway systems, station safety is a critical aspect of the overall structure, and yet, accidents at stations still occur. It is time to learn from these errors and improve conventional methods by utilizing the latest technology, such as machine learning (ML), to analyse accidents and enhance safety systems. ML has been employed in many fields, including engineering systems, and it interacts with us throughout our daily lives. Thus, we must consider the available technology in general and ML in particular in the context of safety in the railway industry. This paper explores the employment of the decision tree (DT) method in safety classification and the analysis of accidents at railway stations to predict the traits of passengers affected by accidents. The critical contribution of this study is the presentation of ML and an explanation of how this technique is applied for ensuring safety, utilizing automated processes, and gaining benefits from this powerful technology. To apply and explore this method, a case study has been selected that focuses on the fatalities caused by accidents at railway stations. An analysis of some of these fatal accidents as reported by the Rail Safety and Standards Board (RSSB) is performed and presented in this paper to provide a broader summary of the application of supervised ML for improving safety at railway stations. Finally, this research shows the vast potential of the innovative application of ML in safety analysis for the railway industry

A Framework for the Verification and Validation of Artificial Intelligence Machine Learning Systems

An effective verification and validation (V&V) process framework for the white-box and black-box testing of artificial intelligence (AI) machine learning (ML) systems is not readily available. This research uses grounded theory to develop a framework that leads to the most effective and informative white-box and black-box methods for the V&V of AI ML systems. Verification of the system ensures that the system adheres to the requirements and specifications developed and given by the major stakeholders, while validation confirms that the system properly performs with representative users in the intended environment and does not perform in an unexpected manner. Beginning with definitions, descriptions, and examples of ML processes and systems, the research results identify a clear and general process to effectively test these systems. The developed framework ensures the most productive and accurate testing results. Formerly, and occasionally still, the system definition and requirements exist in scattered documents that make it difficult to integrate, trace, and test through V&V. Modern system engineers along with system developers and stakeholders collaborate to produce a full system model using model-based systems engineering (MBSE). MBSE employs a Unified Modeling Language (UML) or System Modeling Language (SysML) representation of the system and its requirements that readily passes from each stakeholder for system information and additional input. The comprehensive and detailed MBSE model allows for direct traceability to the system requirements. xxiv To thoroughly test a ML system, one performs either white-box or black-box testing or both. Black-box testing is a testing method in which the internal model structure, design, and implementation of the system under test is unknown to the test engineer. Testers and analysts are simply looking at performance of the system given input and output. White-box testing is a testing method in which the internal model structure, design, and implementation of the system under test is known to the test engineer. When possible, test engineers and analysts perform both black-box and white-box testing. However, sometimes testers lack authorization to access the internal structure of the system. The researcher captures this decision in the ML framework. No two ML systems are exactly alike and therefore, the testing of each system must be custom to some degree. Even though there is customization, an effective process exists. This research includes some specialized methods, based on grounded theory, to use in the testing of the internal structure and performance. Through the study and organization of proven methods, this research develops an effective ML V&V framework. Systems engineers and analysts are able to simply apply the framework for various white-box and black-box V&V testing circumstances

Optimisation approaches for data mining in biological systems

The advances in data acquisition technologies have generated massive amounts of data that present considerable challenge for analysis. How to efficiently and automatically mine through the data and extract the maximum value by identifying the hidden patterns is an active research area, called data mining. This thesis tackles several problems in data mining, including data classification, regression analysis and community detection in complex networks, with considerable applications in various biological systems. First, the problem of data classification is investigated. An existing classifier has been adopted from literature and two novel solution procedures have been proposed, which are shown to improve the predictive accuracy of the original method and significantly reduce the computational time. Disease classification using high throughput genomic data is also addressed. To tackle the problem of analysing large number of genes against small number of samples, a new approach of incorporating extra biological knowledge and constructing higher level composite features for classification has been proposed. A novel model has been introduced to optimise the construction of composite features. Subsequently, regression analysis is considered where two piece-wise linear regression methods have been presented. The first method partitions one feature into multiple complementary intervals and ts each with a distinct linear function. The other method is a more generalised variant of the previous one and performs recursive binary partitioning that permits partitioning of multiple features. Lastly, community detection in complex networks is investigated where a new optimisation framework is introduced to identify the modular structure hidden in directed networks via optimisation of modularity. A non-linear model is firstly proposed before its linearised variant is presented. The optimisation framework consists of two major steps, including solving the non-linear model to identify a coarse initial partition and a second step of solving repeatedly the linearised models to re fine the network partition

Meta-level learning for the effective reduction of model search space.

The exponential growth of volume, variety and velocity of the data is raising the need for investigation of intelligent ways to extract useful patterns from the data. It requires deep expert knowledge and extensive computational resources to find the mapping of learning methods that leads to the optimized performance on a given task. Moreover, numerous configurations of these learning algorithms add another level of complexity. Thus, it triggers the need for an intelligent recommendation engine that can advise the best learning algorithm and its configurations for a given task. The techniques that are commonly used by experts are; trial-and-error, use their prior experience on the specific domain, etc. These techniques sometimes work for less complex tasks that require thousands of parameters to learn. However, the state-of-the-art models, e.g. deep learning models, require well-tuned hyper-parameters to learn millions of parameters which demand specialized skills and numerous computationally expensive and time-consuming trials. In that scenario, Meta-level learning can be a potential solution that can recommend the most appropriate options efficiently and effectively regardless of the complexity of data. On the contrary, Meta-learning leads to several challenges; the most critical ones being model selection and hyper-parameter optimization. The goal of this research is to investigate model selection and hyper-parameter optimization approaches of automatic machine learning in general and the challenges associated with them. In machine learning pipeline there are several phases where Meta-learning can be used to effectively facilitate the best recommendations including 1) pre-processing steps, 2) learning algorithm or their combination, 3) adaptivity mechanism parameters, 4) recurring concept extraction, and 5) concept drift detection. The scope of this research is limited to feature engineering for problem representation, and learning strategy for algorithm and its hyper-parameters recommendation at Meta-level. There are three studies conducted around the two different approaches of automatic machine learning which are model selection using Meta-learning and hyper-parameter optimization. The first study evaluates the situation in which the use of additional data from a different domain can improve the performance of a meta-learning system for time-series forecasting, with focus on cross- domain Meta-knowledge transfer. Although the experiments revealed limited room for improvement over the overall best base-learner, the meta-learning approach turned out to be a safe choice, minimizing the risk of selecting the least appropriate base-learner. There are only 2% of cases recommended by meta- learning that are the worst performing base-learning methods. The second study proposes another efficient and accurate domain adaption approach but using a different meta-learning approach. This study empirically confirms the intuition that there exists a relationship between the similarity of the two different tasks and the depth of network needed to fine-tune in order to achieve accuracy com- parable with that of a model trained from scratch. However, the approach is limited to a single hyper-parameter which is fine-tuning of the network depth based on task similarity. The final study of this research has expanded the set of hyper-parameters while implicitly considering task similarity at the intrinsic dynamics of the training process. The study presents a framework to automatically find a good set of hyper-parameters resulting in reasonably good accuracy, by framing the hyper-parameter selection and tuning within the reinforcement learning regime. The effectiveness of a recommended tuple can be tested very quickly rather than waiting for the network to converge. This approach produces accuracy close to the state-of-the-art approach and is found to be comparatively 20% less computationally expensive than previous approaches. The proposed methods in these studies, belonging to different areas of automatic machine learning, have been thoroughly evaluated on a number of benchmark datasets which confirmed the great potential of these methods

Prediction of student success: A smart data-driven approach

Predicting student’s academic performance is one of the subjects related to the Educational Data Mining process, which intends to extract useful information and new patterns from educational data. Understanding the drivers of student success may assist educators in developing pedagogical methods providing a tool for personalized feedback and advice. In order to improve the academic performance of students and create a decision support solution for higher education institutes, this dissertation proposed a methodology that uses educational data mining to compare prediction models for the students' success. Data belongs to ISCTE master students, a Portuguese university, during 2012 to 2022 academic years. In addition, it was studied which factors are the strongest predictors of the student’s success. PyCaret library was used to compare the performance of several algorithms. Factors that were proposed to influence the success include, for example, the student's gender, previous educational background, the existence of a special statute, and the parents' educational degree. The analysis revealed that the Light Gradient Boosting Machine Classifier had the best performance with an accuracy of 87.37%, followed by Gradient Boosting Classifier (accuracy = 85.11%) and Adaptive Boosting Classifier (accuracy = 83.37%). Hyperparameter tunning improved the performance of all the algorithms. Feature importance analysis revealed that the factors that impacted the student’s success most were the average grade, master time, and the gap between degrees, i.e., the number of years between the last degree and the start of the master.A previsão do sucesso académico de estudantes é um dos tópicos relacionados com a mineração de dados educacionais, a qual pretende extrair informação útil e encontrar padrões a partir de dados académicos. Compreender que fatores afetam o sucesso dos estudantes pode ajudar, as instituições de educação, no desenvolvimento de métodos pedagógicos, dando uma ferramenta de feedback e aconselhamento personalizado. Com o fim de melhorar o desempenho académico dos estudantes e criar uma solução de apoio à decisão, para instituições de ensino superior, este artigo propõe uma metodologia que usa mineração de dados para comparar modelos de previsão para o sucesso dos alunos. Os dados pertencem a alunos de mestrado que frequentaram o ISCTE, uma universidade portuguesa, durante os anos letivos de 2012 a 2022. Além disso, foram estudados quais os fatores que mais afetam o sucesso do aluno. Os vários algoritmos foram comparados pela biblioteca PyCaret. Alguns dos fatores que foram propostos como relevantes para o sucesso incluem, o género do aluno, a formação educacional anterior, a existência de um estatuto especial e o grau de escolaridade dos pais. A análise dos resultados demonstrou que o classificador Light Gradient Boosting Machine (LGBMC) é o que tem o melhor desempenho com uma accuracy de 87.37%, seguindo-se o classificador Gradient Boosting Classifier (accuracy=85.11%) e o classificador Adaptive Boosting (accuracy=83.37%). A afinação de hiperparâmetros melhorou o desempenho de todos os algoritmos. As variáveis que demonstraram ter maior impacto foram a média dos estudantes, a duração do mestrado e o intervalo entre estudos

Creation of a web application for smart park system and parking prediction study for system integration

In the last few years, we have observed an exponential increasing of the information systems, and parking information is one more example of them. The needs of obtaining reliable and updated information of parking slots availability are very important in the goal of traffic reduction. Also parking slot prediction is a new topic that has already started to be applied. San Francisco in America and Santander in Spain are examples of such projects carried out to obtain this kind of information. The aim of this thesis is the study and evaluation of methodologies for parking slot prediction and the integration in a web application, where all kind of users will be able to know the current parking status and also future status according to parking model predictions. The source of the data is ancillary in this work but it needs to be understood anyway to understand the parking behaviour. Actually, there are many modelling techniques used for this purpose such as time series analysis, decision trees, neural networks and clustering. In this work, the author explains the best techniques at this work, analyzes the result and points out the advantages and disadvantages of each one. The model will learn the periodic and seasonal patterns of the parking status behaviour, and with this knowledge it can predict future status values given a date. The data used comes from the Smart Park Ontinyent and it is about parking occupancy status together with timestamps and it is stored in a database. After data acquisition, data analysis and pre-processing was needed for model implementations. The first test done was with the boosting ensemble classifier, employed over a set of decision trees, created with C5.0 algorithm from a set of training samples, to assign a prediction value to each object. In addition to the predictions, this work has got measurements error that indicates the reliability of the outcome predictions being correct. The second test was done using the function fitting seasonal exponential smoothing tbats model. Finally as the last test, it has been tried a model that is actually a combination of the previous two models, just to see the result of this combination. The results were quite good for all of them, having error averages of 6.2, 6.6 and 5.4 in vacancies predictions for the three models respectively. This means from a parking of 47 places a 10% average error in parking slot predictions. This result could be even better with longer data available. In order to make this kind of information visible and reachable from everyone having a device with internet connection, a web application was made for this purpose. Beside the data displaying, this application also offers different functions to improve the task of searching for parking. The new functions, apart from parking prediction, were: - Park distances from user location. It provides all the distances to user current location to the different parks in the city. - Geocoding. The service for matching a literal description or an address to a concrete location. - Geolocation. The service for positioning the user. - Parking list panel. This is not a service neither a function, is just a better visualization and better handling of the information

Using machine learning to improve dense and sparse matrix multiplication kernels

This work is comprised of two different projects in numerical linear algebra. The first project is about using machine learning to speed up dense matrix-matrix multiplication computations on a shared-memory computer architecture. We found that found basic loop-based matrix-matrix multiplication algorithms tied to a decision tree algorithm selector were competitive to using Intel\u27s Math Kernel Library for the same computation. The second project is a preliminary report about re-implementing an encoding format for spare matrix-vector multiplication called Compressed Spare eXtended (CSX). The goal for the second project is to use machine learning to aid in encoding matrix substructures in the CSX format without using exhaustive search and a Just-In-Time compiler

A Survey on Automatic Parameter Tuning for Big Data Processing Systems

Big data processing systems (e.g., Hadoop, Spark, Storm) contain a vast number of configuration parameters controlling parallelism, I/O behavior, memory settings, and compression. Improper parameter settings can cause significant performance degradation and stability issues. However, regular users and even expert administrators grapple with understanding and tuning them to achieve good performance. We investigate existing approaches on parameter tuning for both batch and stream data processing systems and classify them into six categories: rule-based, cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. We summarize the pros and cons of each approach and raise some open research problems for automatic parameter tuning.Peer reviewe