An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests

Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, that can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine and bioinformatics within the past few years. High dimensional problems are common not only in genetics, but also in some areas of psychological research, where only few subjects can be measured due to time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications, and provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated using freely available implementations in the R system for statistical computing

Meta-Learning and the Full Model Selection Problem

When working as a data analyst, one of my daily tasks is to select appropriate tools from a set of existing data analysis techniques in my toolbox, including data preprocessing, outlier detection, feature selection, learning algorithm and evaluation techniques, for a given data project. This indeed was an enjoyable job at the beginning, because to me finding patterns and valuable information from data is always fun. Things become tricky when several projects needed to be done in a relatively short time. Naturally, as a computer science graduate, I started to ask myself, "What can be automated here?"; because, intuitively, part of my work is more or less a loop that can be programmed. Literally, the loop is "choose, run, test and choose again... until some criterion/goals are met". In other words, I use my experience or knowledge about machine learning and data mining to guide and speed up the process of selecting and applying techniques in order to build a relatively good predictive model for a given dataset for some purpose. So the following questions arise: "Is it possible to design and implement a system that helps a data analyst to choose from a set of data mining tools? Or at least that provides a useful recommendation about tools that potentially save some time for a human analyst." To answer these questions, I decided to undertake a long-term study on this topic, to think, define, research, and simulate this problem before coding my dream system. This thesis presents research results, including new methods, algorithms, and theoretical and empirical analysis from two directions, both of which try to propose systematic and efficient solutions to the questions above, using different resource requirements, namely, the meta-learning-based algorithm/parameter ranking approach and the meta-heuristic search-based full-model selection approach. Some of the results have been published in research papers; thus, this thesis also serves as a coherent collection of results in a single volume

Generating Compact Tree Ensembles via Annealing

Tree ensembles are flexible predictive models that can capture relevant variables and to some extent their interactions in a compact and interpretable manner. Most algorithms for obtaining tree ensembles are based on versions of boosting or Random Forest. Previous work showed that boosting algorithms exhibit a cyclic behavior of selecting the same tree again and again due to the way the loss is optimized. At the same time, Random Forest is not based on loss optimization and obtains a more complex and less interpretable model. In this paper we present a novel method for obtaining compact tree ensembles by growing a large pool of trees in parallel with many independent boosting threads and then selecting a small subset and updating their leaf weights by loss optimization. We allow for the trees in the initial pool to have different depths which further helps with generalization. Experiments on real datasets show that the obtained model has usually a smaller loss than boosting, which is also reflected in a lower misclassification error on the test set.Comment: Comparison with Random Forest included in the results sectio

A comparison of AdaBoost algorithms for time series forecast combination

Recently, combination algorithms from machine learning classification have been extended to time series regression, most notably seven variants of the popular AdaBoost algorithm. Despite their theoretical promise their empirical accuracy in forecasting has not yet been assessed, either against each other or against any established approaches of forecast combination, model selection, or statistical benchmark algorithms. Also, none of the algorithms have been assessed on a representative set of empirical data, using only few synthetic time series. We remedy this omission by conducting a rigorous empirical evaluation using a representative set of 111 industry time series and a valid and reliable experimental design. We develop a full-factorial design over derived Boosting meta-parameters, creating 42 novel Boosting variants, and create a further 47 novel Boosting variants using research insights from forecast combination. Experiments show that only few Boosting meta-parameters increase accuracy, while meta-parameters derived from forecast combination research outperform others