Statistical aspects of credit scoring

This thesis is concerned with statistical aspects of credit scoring, the process of determining how likely an applicant for credit is to default with repayments. In Chapters 1-4 a detailed introduction to credit scoring methodology is presented, including evaluation of previous published work on credit scoring and a review of discrimination and classification techniques. In Chapter 5 we describe different approaches to measuring the absolute and relative performance of credit scoring models. Two significance tests are proposed for comparing the bad rate amongst the accepts (or the error rate) from two classifiers. In Chapter 6 we consider different approaches to reject inference, the procedure of allocating class membership probabilities to the rejects. One reason for needing reject inference is to reduce the sample selection bias that results from using a sample consisting only of accepted applicants to build new scorecards. We show that the characteristic vectors for the rejects do not contain information about the parameters of the observed data likelihood, unless extra information or assumptions are included. Methods of reject inference which incorporate additional information are proposed. In Chapter 7 we make comparisons of a range of different parametric and nonparametric classification techniques for credit scoring: linear regression, logistic regression, projection pursuit regression, Poisson regression, decision trees and decision graphs. We conclude that classifier performance is fairly insensitive to the particular technique adopted. In Chapter 8 we describe the application of the k-NN method to credit scoring. We propose using an adjusted version of the Eucidean distance metric, which is designed to incorporate knowledge of class separation contained in the data. We evaluate properties of the k-NN classifier through empirical studies and make comparisons with existing techniques

Robust Energy Consumption Prediction with a Missing Value-Resilient Metaheuristic-based Neural Network in Mobile App Development

Energy consumption is a fundamental concern in mobile application development, bearing substantial significance for both developers and end-users. Moreover, it is a critical determinant in the consumer's decision-making process when considering a smartphone purchase. From the sustainability perspective, it becomes imperative to explore approaches aimed at mitigating the energy consumption of mobile devices, given the significant global consequences arising from the extensive utilisation of billions of smartphones, which imparts a profound environmental impact. Despite the existence of various energy-efficient programming practices within the Android platform, the dominant mobile ecosystem, there remains a need for documented machine learning-based energy prediction algorithms tailored explicitly for mobile app development. Hence, the main objective of this research is to propose a novel neural network-based framework, enhanced by a metaheuristic approach, to achieve robust energy prediction in the context of mobile app development. The metaheuristic approach here plays a crucial role in not only identifying suitable learning algorithms and their corresponding parameters but also determining the optimal number of layers and neurons within each layer. To the best of our knowledge, prior studies have yet to employ any metaheuristic algorithm to address all these hyperparameters simultaneously. Moreover, due to limitations in accessing certain aspects of a mobile phone, there might be missing data in the data set, and the proposed framework can handle this. In addition, we conducted an optimal algorithm selection strategy, employing 13 metaheuristic algorithms, to identify the best algorithm based on accuracy and resistance to missing values. The comprehensive experiments demonstrate that our proposed approach yields significant outcomes for energy consumption prediction.Comment: The paper is submitted to a related journa

Applications Of Machine Learning In Biology And Medicine

Machine learning as a field is defined to be the set of computational algorithms that improve their performance by assimilating data. As such, the field as a whole has found applications in many diverse disciplines from robotics and communication in engineering to economics and finance, and also biology and medicine. It should not come as a surprise that many popular methods in use today have completely different origins. Despite this heterogeneity, different methods can be divided into standard tasks, such as supervised, unsupervised, semi-supervised and reinforcement learning. Although machine learning as a field can be formalized as methods trying to solve certain standard tasks, applying these tasks on datasets from different fields comes with certain caveats, and sometimes is fraught with challenges. In this thesis, we develop general procedures and novel solutions, dealing with practical problems that arise when modeling biological and medical data. Cost sensitive learning is an important area of research in machine learning which addresses the widespread and practical problem of dealing with different costs during the learning and deployment of classification algorithms. In many applications such as credit fraud detection, network intrusion and specifically medical diagnosis domains, prior class distributions are highly skewed, which makes the training examples very much unbalanced. Combining this with uneven misclassification costs renders standard machine learning approaches useless in learning an acceptable decision function. We experimentally show the benefits and shortcomings of various methods that convert cost blind learning algorithms to cost sensitive ones. Using the results and best practices found for cost sensitive learning, we design and develop a machine learning approach to ontology mapping. Next, we present a novel approach to deal with uncertainty in classification when costs are unknown or otherwise hard to assign. Support Vector Machines (SVM) are considered to be among the most successful approaches for classification. However prediction of instances near the decision boundary depends more on the specific parameter selection or noise in data, rather than a clear difference in features. In many applications such as medical diagnosis, these regions should be labeled as uncertain rather than assigned to any particular class. Furthermore, instances may belong to novel disease subtypes that are not from any previously known class. In such applications, declining to make a prediction could be beneficial when more powerful but expensive tests are available. We develop a novel approach for optimal selection of the threshold and show its successful application on three biological and medical datasets. The last part of this thesis provides novel solutions for handling high dimensional data. Although high-dimensional data is ubiquitously found in many disciplines, current life science research almost always involves high-dimensional genomics/proteomics data. The ``omics\u27\u27 data provide a wealth of information and have changed the research landscape in biology and medicine. However, these data are plagued with noise, redundancy and collinearity, which makes the discovery process very difficult and costly. Any method that can accurately detect irrelevant and noisy variables in omics data would be highly valuable. We present Robust Feature Selection (RFS), a randomized feature selection approach dedicated to low-sample high-dimensional data. RFS combines an embedded feature selection method with a randomization procedure for stability. Recent advances in sparse recovery and estimation methods have provided efficient and asymptotically consistent feature selection algorithms. However, these methods lack finite sample error control due to instability. Furthermore, the chances of correct recovery diminish with more collinearity among features. To overcome these difficulties, RFS uses a randomization procedure to provide an accurate and stable feature selection method. We thoroughly evaluate RFS by comparing it to a number of popular univariate and multivariate feature selection methods and show marked prediction accuracy improvement of a diagnostic signature, while preserving a good stability

Criminal data analysis based on low rank sparse representation

FINDING effective clustering methods for a high dimensional dataset is challenging due to the curse of dimensionality. These challenges can usually make the most of basic common algorithms fail in highdimensional spaces from tackling problems such as large number of groups, and overlapping. Most domains uses some parameters to describe the appearance, geometry and dynamics of a scene. This has motivated the implementation of several techniques of a high-dimensional data for finding a low-dimensional space. Many proposed methods fail to overcome the challenges, especially when the data input is high-dimensional, and the clusters have a complex. REGULARLY in high dimensional data, lots of the data dimensions are not related and might hide the existing clusters in noisy data. High-dimensional data often reside on some low dimensional subspaces. The problem of subspace clustering algorithms is to uncover the type of relationship of an objects from one dimension that are related in different subsets of another dimensions. The state-of-the-art methods for subspace segmentation which included the Low Rank Representation (LRR) and Sparse Representation (SR). The former seeks the global lowest-rank representation but restrictively assumes the independence among subspaces, whereas the latter seeks the clustering of disjoint or overlapped subspaces through locality measure, which, however, causes failure in the case of large noise. THIS thesis aims are to identify the key problems and obstacles that have challenged the researchers in recent years in clustering high dimensional data, then to implement an effective subspace clustering methods for solving high dimensional crimes domains for both real events and synthetic data which has complex data structure with 168 different offence crimes. As well as to overcome the disadvantages of existed subspace algorithms techniques. To this end, a Low-Rank Sparse Representation (LRSR) theory, the future will refer to as Criminal Data Analysis Based on LRSR will be examined, then to be used to recover and segment embedding subspaces. The results of these methods will be discussed and compared with what already have been examined on previous approaches such as K-mean and PCA segmented based on K-means. The previous approaches have helped us to chose the right subspace clustering methods. The Proposed method based on subspace segmentation method named Low Rank subspace Sparse Representation (LRSR) which not only recovers the low-rank subspaces but also gets a relatively sparse segmentation with respect to disjoint subspaces or even overlapping subspaces. BOTH UCI Machine Learning Repository, and crime database are the best to find and compare the best subspace clustering algorithm that fit for high dimensional space data. We used many Open-Source Machine Learning Frameworks and Tools for both employ our machine learning tasks and methods including preparing, transforming, clustering and visualizing the high-dimensional crime dataset, we precisely have used the most modern and powerful Machine Learning Frameworks data science that known as SciKit-Learn for library for the Python programming language, as well as we have used R, and Matlab in previous experiment

Towards more reliable feature evaluations for classification

In this thesis we study feature subset selection and feature weighting algorithms. Our aim is to make their output more stable and more useful when used to train a classifier. We begin by defining the concept of stability and selecting a measure to asses the output of the feature selection process. Then we study different sources of instability and propose modifications of classic algorithms that improve their stability. We propose a modification of wrapper algorithms that take otherwise unused information into account to overcome an intrinsic source of instability for this algorithms: the feature assessment being a random variable that depends on the particular training subsample. Our version accumulates the evaluation results of each feature at each iteration to average out the effect of the randomness. Another novel proposal is to make wrappers evaluate the remainder set of features at each step to overcome another source of instability: randomness of the algorithms themselves. In this case, by evaluating the non-selected set of features, the initial choice of variables is more educated. These modifications do not bring a great amount of computational overhead and deliver better results, both in terms of stability and predictive power. We finally tackle another source of instability: the differential contribution of the instances to feature assessment. We present a framework to combine almost any instance weighting algorithm with any feature weighting one. Our combination of algorithms deliver more stable results for the various feature weighting algorithms we have tested. Finally, we present a deeper integration of instance weighting with feature weighting by modifying the Simba algorithm, that delivers even better results in terms of stabilityEl focus d'aquesta tesi és mesurar, estudiar i millorar l’estabilitat d’algorismes de selecció de subconjunts de variables (SSV) i avaluació de variables (AV) en un context d'aprenentatge supervisat. El propòsit general de la SSV en un context de classificació és millorar la precisió de la predicció. Nosaltres afirmem que hi ha un altre gran repte en SSV i AV: l’estabilitat des resultats. Un cop triada una mesura d’estabilitat entre les estudiades, proposem millores d’un algorisme molt popular: el Relief. Analitzem diferents mesures de distància a més de la original i estudiem l'efecte que tenen sobre la precisió, la detecció de la redundància i l'estabilitat. També posem a prova diferents maneres d’utilitzar els pesos que es calculen a cada pas per influir en el càlcul de distàncies d’una manera similar a com ho fa un altre algorisme d'AV: el Simba. També millorem la seva estabilitat incrementant la contribució dels pesos de les variables en el càlcul de la distància a mesura que avança el temps per minimitzar l’impacte de la selecció aleatòria de les primeres instàncies. Pel què fa als algorismes embolcall, (wrappers) els modifiquem per tenir en compte informació que era ignorada per superar una font intrínseca d’inestabilitat: el fet que l’avaluació de les variables és una variable aleatòria que depèn del subconjunt de dades utilitzat. La nostra versió acumula els resultats en cada iteració per compensar l’efecte aleatori mentre que els originals descarten tota la informació recollida sobre cada variable en una determinada iteració i comencen de nou a la següent, donant lloc a resultats més inestables. Una altra proposta és fer que aquests wrappers avaluïn el subconjunt de variables no seleccionat en cada iteració per evitar una altra font d’inestabilitat. Aquestes modificacions no comporten un gran augment de cost computacional i els seus resultats són més estables i més útils per un classificador. Finalment proposem ponderar la contribució de cada instància en l’AV. Poden existir observacions atípiques que no s'haurien de tenir tant en compte com les altres; si estem intentant predir un càncer utilitzant informació d’anàlisis genètics, hauríem de donar menys credibilitat a les dades obtingudes de persones exposades a grans nivells de radiació tot i que no tenir informació sobre aquesta exposició. Els mètodes d’avaluació d’instàncies (AI) pretenen identificar aquests casos i assignar-los pesos més baixos. Varis autors han treballat en esquemes d’AI per millorar la SSV però no hi ha treball previ en la combinació d'AI amb AV. Presentem un marc de treball per combinar algorismes d'AI amb altres d'AV. A més proposem un nou algorisme d’AI basat en el concepte de marge de decisió que utilitzen alguns algorismes d’AV. Amb aquest marc de treball hem posat a prova les modificacions contra les versions originals utilitzant varis jocs de dades del repositori UCI, de xips d'ADN i els utilitzats en el desafiament de SSV del NIPS-2003. Les nostres combinacions d'algorismes d'avaluació d'instàncies i atributs ens aporten resultats més estables per varis algorismes d'avaluació d'atributs que hem estudiat. Finalment, presentem una integració més profunda de l'avaluació d'instàncies amb l'algorisme de selecció de variables Simba consistent a utilitzar els pesos de les instàncies per ponderar el càlcul de les distàncies, amb la que obtenim resultats encara millors en termes d’estabilitat. Les contribucions principals d’aquesta tesi son: (i) aportar un marc de treball per combinar l'AI amb l’AV, (ii) una revisió de les mesures d’estabilitat de SSV, (iii) diverses modificacions d’algorismes de SSV i AV que milloren la seva estabilitat i el poder predictiu del subconjunt de variables seleccionats; sense un augment significatiu del seu cost computacional, (iv) una definició teòrica de la importància d'una variable i (v) l'estudi de la relació entre l'estabilitat de la SSV i la redundància de les variables.Postprint (published version

Probabilistic models of perception

Mental representations of objects may fluctuate or change from moment to moment. Many models of similarity, identification, classification, and preferential choice are deterministic. These models cannot formally account for perceptual fluctuations. In this thesis, it is assumed that there exists a probability density function for psychological magnitudes (usually assumed to be multivariate normal) and a judgment function which defines how these magnitudes are used to make a particular decision. Based on these ideas, probabilistic models of triad discrimination, similarity, identification and preferential choice are derived and evaluated. Several of these models can account for differences in self-similarity, asymmetric similarities and violations of the triangle inequality because the metric axioms are not assumed to apply to proximity measures among stimulus means. A paradox, created when deterministic models of identification are compared, concerning the universal form of the similarity function and the distance metric, is resolved using a probabilistic model. The use of nonlinear least squares to estimate parameters is illustrated in the case of several of the models. Fechner-Thurstone models, in which stimulus variability, a psychophysical transformation, and psychological variability are formally included, are discussed