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This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.
During the last few decades, the advancements in technology we witnessed have considerably improved our capacities to collect and store large amount of information. As a consequence, they enhanced our data mining potential. The repercussions, on multiple scientific fields, have been stark. In statistical analysis for example, many results derived under the then common low dimensional framework, where the number of covariates is smaller than the size of the dataset, had to be extended. The literature now abounds with significant contributions in high dimensional settings. Following this path, the current thesis touches on the concept of variable importance that is, a methodology used to assess the significance of a variable. It is a focal point in today's era of big data. As an example, it is often use for prediction models in high dimensional settings to select the main predictors. Our contributions can be divided in three parts.In the first part of the thesis, we rely on semiparametric models for our analysis. We introduce a multivariate variable importance measure, defined as a sound statistical parameter, which is complemented by user defined marginal structural models. It allows one to quantify the significance of an exposure on a response while taking into account all other covariates. The parameter is studied through the Targeted Minimum Loss Estimation (TMLE) methodology. We perform its full theoretical analysis. We are able to establish consistency and asymptotic results which provide as a consequence p-values for hypothesis testing of the parameter of interest. A numerical analysis is conducted to illustrate theoretical results. It is achieved by extending the implementation of the TMLE.NPVI package such that it is able to cope with multivariate parameter.In the second part, we introduce a variable importance measure which is defined through a nonparametric regression model under a high dimensional framework. It is partially derived from the parameter described in the first part of the thesis, without the requirement that the user provides a marginal structural model. The regression model comes with the caveat of having a data structure which, in some cases, is subject to measurement errors. Using a high-dimensional projection on an orthonormal base such as Fourier series, smoothing splines and the Lasso methodology, we establish consistency and the convergence rates of our estimators. We further discuss how these rates are affected when the design of the dataset is polluted. A numerical study, based on simulated and on financial datasets, is provided.In the third and final part of this thesis, we consider a variable importance measure defined through a linear regression model subject to errors-in-variables. This regression model was derived in the previous chapter. The estimation of the parameter of interest is done through a convex optimization problem, obtained by projecting the empirical covariance estimator on the set of symmetric non-negative matrices, and using the Slope methodology. We perform its complete theoretical and numerical analysis. We establish sufficient conditions, rather restrictive on the noise variables, under which to attain optimal convergence rates for the parameter of interest and discuss the impact of measurement errors on these rates.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Advances in computing hardware and algorithms have dramatically improved the ability to simulate complex processes computationally. Today's simulation capabilities offer the prospect of addressing questions that in the past could be addressed only by resource-intensive experimentation, if at all. Assessing the Reliability of Complex Models recognizes the ubiquity of uncertainty in computational estimates of reality and the necessity for its quantification. As computational science and engineering have matured, the process of quantifying or bounding uncertainties in a computational estimate of a physical quality of interest has evolved into a small set of interdependent tasks: verification, validation, and uncertainty of quantification (VVUQ). In recognition of the increasing importance of computational simulation and the increasing need to assess uncertainties in computational results, the National Research Council was asked to study the mathematical foundations of VVUQ and to recommend steps that will ultimately lead to improved processes. Assessing the Reliability of Complex Models discusses changes in education of professionals and dissemination of information that should enhance the ability of future VVUQ practitioners to improve and properly apply VVUQ methodologies to difficult problems, enhance the ability of VVUQ customers to understand VVUQ results and use them to make informed decisions, and enhance the ability of all VVUQ stakeholders to communicate with each other. This report is an essential resource for all decision and policy makers in the field, students, stakeholders, UQ experts, and VVUQ educators and practitioners.
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
Classification and regression problems characterized by the number (p) of predictor variables being relatively large to the sample size (n), called 'the large p small n problem', are common in educational sciences. Variable selection methods can resolve the problem by reducing variable dimensionality while maintaining prediction accuracy. However, traditional statistical approaches, such as stepwise regression models, cannot deal with the large p small n problem effectively. In this dissertation, I introduce variable importance measures (VIMs) from decision tree models to educational research to select a parsimonious classification model as well as evaluate their properties under different conditions. I also propose new VIMs based on Bayesian model averaging (BMA) and a hybrid approach of random forests and BMA (RF-BMA). In addition, I propose a cross-validated permutation (CV-permutation) threshold for random forest VIMs to identify informative variables. Using classification models, a series of simulation studies is conducted with four simulation factors: 10 VIM methods, five data models, two numbers of variables, and two samples sizes. Each combination of the four factors was replicated 1,000 times. In addition to the simulation studies, VIMs were applied to an education longitudinal study to select influential predictor variables on the prediction of six-year college graduation. As evaluation measures of VIMs, effectiveness, rank, and Brier score were used. This dissertation finds that random forests VIMs with the CV-permutation threshold performed better in preserving prediction accuracy than the other VIMs under the large p small n condition. Conversely, BMA outperformed tree-based VIMs with regard to the effectiveness of variable selection under the small p large n condition. RF-BMA performed as well as tree VIMs not only in variable selection but also in prediction accuracy under the large p condition. When p> 30, RF-BMA outperformed BMA in variable selection and prediction accuracy. Therefore, RF-BMA can be an attractive alternative VIM to BMA as well as to tree-based VIMs. The case study results showed that most tree VIMs selected the top five cognitive measures. The VIMs from BMA and RF-BMA selected additional variables besides the cognitive measures.