7,101 research outputs found
Comparison of Logistic Regression and Classification Trees to Forecast Short Term Defaults on Repeat Consumer Loans
This dissertation highlights the performance comparison between two popular contemporary consumer loan credit scoring techniques, namely logistic regression and classification trees. Literature has shown logistic regression to perform better than classification trees in terms of predictiveness and robustness when forecasting consumer loan default events over standard twelve-month outcome periods. One of the major shortcomings with classification trees is its tendency to overfit data eroding its robustness, making it vulnerable to underlying population characteristic shifts. Classification trees remains a popular technique due to its ease of application (algorithm machine learning basis) and model interpretation. Past research has found classification trees to perform marginally better than logistic regression with respect to predictiveness and robustness when modelling short term consumer credit default outcomes related to previously unseen new customer credit loan applications. This dissertation independently tested this finding on reloan consumer loan data, repeat customers who renewed loan facilities at a significant South African micro lender. This dissertation tests the finding if the classification tree technique would outperform logistic regression when modelling this new type of loan data. Credit scoring models were built and tested for each respective technique across identical data sets with the intent to eliminate bias. Robustness tests were constructed via careful iterative data splits. Performance tests measuring predictiveness and robustness were conducted via the weighted sums of squared error evaluation approach. Results reveal logistic regression to outperform classification trees on predictiveness and robustness across the designed uniform iterative data splits, which suggests that logistic regression remains the superior technique when modelling short term credit default outcomes on reloan consumer loan data
Advanced Signal Processing and Adaptive Learning Methods
[No abstract available
Creating Full Individual-level Location Timelines from Sparse Social Media Data
In many domain applications, a continuous timeline of human locations is
critical; for example for understanding possible locations where a disease may
spread, or the flow of traffic. While data sources such as GPS trackers or Call
Data Records are temporally-rich, they are expensive, often not publicly
available or garnered only in select locations, restricting their wide use.
Conversely, geo-located social media data are publicly and freely available,
but present challenges especially for full timeline inference due to their
sparse nature. We propose a stochastic framework, Intermediate Location
Computing (ILC) which uses prior knowledge about human mobility patterns to
predict every missing location from an individual's social media timeline. We
compare ILC with a state-of-the-art RNN baseline as well as methods that are
optimized for next-location prediction only. For three major cities, ILC
predicts the top 1 location for all missing locations in a timeline, at 1 and
2-hour resolution, with up to 77.2% accuracy (up to 6% better accuracy than all
compared methods). Specifically, ILC also outperforms the RNN in settings of
low data; both cases of very small number of users (under 50), as well as
settings with more users, but with sparser timelines. In general, the RNN model
needs a higher number of users to achieve the same performance as ILC. Overall,
this work illustrates the tradeoff between prior knowledge of heuristics and
more data, for an important societal problem of filling in entire timelines
using freely available, but sparse social media data.Comment: 10 pages, 8 figures, 2 table
Neural networks in multiphase reactors data mining: feature selection, prior knowledge, and model design
Les rĂ©seaux de neurones artificiels (RNA) suscitent toujours un vif intĂ©rĂȘt dans la plupart des domaines dâingĂ©nierie non seulement pour leur attirante « capacitĂ© dâapprentissage » mais aussi pour leur flexibilitĂ© et leur bonne performance, par rapport aux approches classiques. Les RNA sont capables «dâapproximer» des relations complexes et non linĂ©aires entre un vecteur de variables dâentrĂ©es x et une sortie y. Dans le contexte des rĂ©acteurs multiphasiques le potentiel des RNA est Ă©levĂ© car la modĂ©lisation via la rĂ©solution des Ă©quations dâĂ©coulement est presque impossible pour les systĂšmes gaz-liquide-solide. Lâutilisation des RNA dans les approches de rĂ©gression et de classification rencontre cependant certaines difficultĂ©s. Un premier problĂšme, gĂ©nĂ©ral Ă tous les types de modĂ©lisation empirique, est celui de la sĂ©lection des variables explicatives qui consiste Ă dĂ©cider quel sous-ensemble xs â x des variables indĂ©pendantes doit ĂȘtre retenu pour former les entrĂ©es du modĂšle. Les autres difficultĂ©s Ă surmonter, plus spĂ©cifiques aux RNA, sont : le sur-apprentissage, lâambiguĂŻtĂ© dans lâidentification de lâarchitecture et des paramĂštres des RNA et le manque de comprĂ©hension phĂ©nomĂ©nologique du modĂšle rĂ©sultant. Ce travail se concentre principalement sur trois problĂ©matiques dans lâutilisation des RNA: i) la sĂ©lection des variables, ii) lâutilisation de la connaissance apriori, et iii) le design du modĂšle. La sĂ©lection des variables, dans le contexte de la rĂ©gression avec des groupes adimensionnels, a Ă©tĂ© menĂ©e avec les algorithmes gĂ©nĂ©tiques. Dans le contexte de la classification, cette sĂ©lection a Ă©tĂ© faite avec des mĂ©thodes sĂ©quentielles. Les types de connaissance a priori que nous avons insĂ©rĂ©s dans le processus de construction des RNA sont : i) la monotonie et la concavitĂ© pour la rĂ©gression, ii) la connectivitĂ© des classes et des coĂ»ts non Ă©gaux associĂ©s aux diffĂ©rentes erreurs, pour la classification. Les mĂ©thodologies dĂ©veloppĂ©es dans ce travail ont permis de construire plusieurs modĂšles neuronaux fiables pour les prĂ©dictions de la rĂ©tention liquide et de la perte de charge dans les colonnes garnies Ă contre-courant ainsi que pour la prĂ©diction des rĂ©gimes dâĂ©coulement dans les colonnes garnies Ă co-courant.Artificial neural networks (ANN) have recently gained enormous popularity in many engineering fields, not only for their appealing âlearning ability, â but also for their versatility and superior performance with respect to classical approaches. Without supposing a particular equational form, ANNs mimic complex nonlinear relationships that might exist between an input feature vector x and a dependent (output) variable y. In the context of multiphase reactors the potential of neural networks is high as the modeling by resolution of first principle equations to forecast sought key hydrodynamics and transfer characteristics is intractable. The general-purpose applicability of neural networks in regression and classification, however, poses some subsidiary difficulties that can make their use inappropriate for certain modeling problems. Some of these problems are general to any empirical modeling technique, including the feature selection step, in which one has to decide which subset xs â x should constitute the inputs (regressors) of the model. Other weaknesses specific to the neural networks are overfitting, model design ambiguity (architecture and parameters identification), and the lack of interpretability of resulting models. This work addresses three issues in the application of neural networks: i) feature selection ii) prior knowledge matching within the models (to answer to some extent the overfitting and interpretability issues), and iii) the model design. Feature selection was conducted with genetic algorithms (yet another companion from artificial intelligence area), which allowed identification of good combinations of dimensionless inputs to use in regression ANNs, or with sequential methods in a classification context. The type of a priori knowledge we wanted the resulting ANN models to match was the monotonicity and/or concavity in regression or class connectivity and different misclassification costs in classification. Even the purpose of the study was rather methodological; some resulting ANN models might be considered contributions per se. These models-- direct proofs for the underlying methodologies-- are useful for predicting liquid hold-up and pressure drop in counter-current packed beds and flow regime type in trickle beds
Exploring the Application of Wearable Movement Sensors in People with Knee Osteoarthritis
People with knee osteoarthritis have difficulty with functional activities, such as walking or get into/out of a chair. This thesis explored the clinical relevance of biomechanics and how wearable sensor technology may be used to assess how people move when their clinician is unable to directly observe them, such as at home or work. The findings of this thesis suggest that artificial intelligence can be used to process data from sensors to provide clinically important information about how people perform troublesome activities
Datadriven Human Intention Analysis : Supported by Virtual Reality and Eye Tracking
The ability to determine an upcoming action or what decision a human is about to take, can be useful in multiple areas, for example in manufacturing where humans working with collaborative robots, where knowing the intent of the operator could provide the robot with important information to help it navigate more safely. Another field that could benefit from a system that provides information regarding human intentions is the field of psychological testing where such a system could be used as a platform for new research or be one way to provide information in the diagnostic process. The work presented in this thesis investigates the potential use of virtual reality as a safe, customizable environment to collect gaze and movement data, eye tracking as the non-invasive system input that gives insight into the human mind, and deep machine learning as the tool that analyzes the data. The thesis defines an experimental procedure that can be used to construct a virtual reality based testing system that gathers gaze and movement data, carries out a test study to gather data from human participants, and implements an artificial neural network in order to analyze human behaviour. This is followed by four studies that gives evidence to the decisions that were made in the experimental procedure and shows the potential uses of such a system
Gaze Based Human Intention Analysis
The ability to determine an upcoming action or what decision a human is about to take, can be useful in multiple areas, for example during human-robot collaboration in manufacturing, where knowing the intent of the operator could provide the robot with important information to help it navigate more safely. Another field that could benefit from a system that provides information regarding human intentions is the field of psychological testing where such a system could be used as a platform for new research or be one way to provide information in the diagnostic process. The work presented in this thesis investigates the potential use of virtual reality as a safe, measurable, and customizable environment to collect gaze and movement data, eye tracking as the non-invasive system input that gives insight into the human mind, and deep machine learning as one tool to analyze the data. The thesis defines an experimental procedure that can be used to construct a virtual reality based testing system that gathers gaze and movement data, carry out a test study to gather data from human participants, and implement artificial neural networks in order to analyze human behaviour. This is followed by two studies that gives evidence to the decisions that were made in the experimental procedure and shows the potential uses of such a system
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