Evolving Ensembles with TPOT

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceMachine learning has become popular in recent years as a solution to various problems such as fraud detection, weather prediction, improve diagnosis accuracy, and more. One of its goals is to find the model that best explains the problem. Among the several alternatives on how to accomplish that, significant attention has been laid on the matter of accuracy using stacking ensembles: the objective is to produce a more accurate prediction by combining the predictions of various estimators. This model has often been exhibiting a superior performance in contrast to its single counterparts. Because the process of choosing the best model for a given problem can be time-consuming, a necessity to automatize the machine learning process has emerged. Different tools allow this, including TPOT, a Python library that uses genetic programming to optimize the machine learning process, evolving pipelines randomly created until the best one is found, or a previously fixed maximum number of generations for the given problem is reached. Genetic programming is a field of machine learning that uses evolutionary algorithms to generate new computer programs, and it has been shown successful in quite a few applications. TPOT uses several machine learning algorithms from the Sklearn Python library. It also features some ensembles, such as Random Forest or AdaBoost. Currently, stacking ensembles are not implemented yet on TPOT, and, considering its current accuracy rates, the objective of this thesis is to implement stacking ensembles in TPOT. After we implemented stacking ensembles successfully in TPOT, we performed some experiments with different datasets and noticed that for almost all of them, TPOT has comparable performance to TPOT with stacking ensembles. Also, we observed that, when using the light dictionary version of TPOT, the results of the Stacking configuration improved for two datasets since it used weaker learners

Data- og ekspertdreven variabelseleksjon for prediktive modeller i helsevesenet : mot økt tolkbarhet i underbestemte maskinlæringsproblemer

Modern data acquisition techniques in healthcare generate large collections of data from multiple sources, such as novel diagnosis and treatment methodologies. Some concrete examples are electronic healthcare record systems, genomics, and medical images. This leads to situations with often unstructured, high-dimensional heterogeneous patient cohort data where classical statistical methods may not be sufficient for optimal utilization of the data and informed decision-making. Instead, investigating such data structures with modern machine learning techniques promises to improve the understanding of patient health issues and may provide a better platform for informed decision-making by clinicians. Key requirements for this purpose include (a) sufficiently accurate predictions and (b) model interpretability. Achieving both aspects in parallel is difficult, particularly for datasets with few patients, which are common in the healthcare domain. In such cases, machine learning models encounter mathematically underdetermined systems and may overfit easily on the training data. An important approach to overcome this issue is feature selection, i.e., determining a subset of informative features from the original set of features with respect to the target variable. While potentially raising the predictive performance, feature selection fosters model interpretability by identifying a low number of relevant model parameters to better understand the underlying biological processes that lead to health issues. Interpretability requires that feature selection is stable, i.e., small changes in the dataset do not lead to changes in the selected feature set. A concept to address instability is ensemble feature selection, i.e. the process of repeating the feature selection multiple times on subsets of samples of the original dataset and aggregating results in a meta-model. This thesis presents two approaches for ensemble feature selection, which are tailored towards high-dimensional data in healthcare: the Repeated Elastic Net Technique for feature selection (RENT) and the User-Guided Bayesian Framework for feature selection (UBayFS). While RENT is purely data-driven and builds upon elastic net regularized models, UBayFS is a general framework for ensembles with the capabilities to include expert knowledge in the feature selection process via prior weights and side constraints. A case study modeling the overall survival of cancer patients compares these novel feature selectors and demonstrates their potential in clinical practice. Beyond the selection of single features, UBayFS also allows for selecting whole feature groups (feature blocks) that were acquired from multiple data sources, as those mentioned above. Importance quantification of such feature blocks plays a key role in tracing information about the target variable back to the acquisition modalities. Such information on feature block importance may lead to positive effects on the use of human, technical, and financial resources if systematically integrated into the planning of patient treatment by excluding the acquisition of non-informative features. Since a generalization of feature importance measures to block importance is not trivial, this thesis also investigates and compares approaches for feature block importance rankings. This thesis demonstrates that high-dimensional datasets from multiple data sources in the medical domain can be successfully tackled by the presented approaches for feature selection. Experimental evaluations demonstrate favorable properties of both predictive performance, stability, as well as interpretability of results, which carries a high potential for better data-driven decision support in clinical practice.Moderne datainnsamlingsteknikker i helsevesenet genererer store datamengder fra flere kilder, som for eksempel nye diagnose- og behandlingsmetoder. Noen konkrete eksempler er elektroniske helsejournalsystemer, genomikk og medisinske bilder. Slike pasientkohortdata er ofte ustrukturerte, høydimensjonale og heterogene og hvor klassiske statistiske metoder ikke er tilstrekkelige for optimal utnyttelse av dataene og god informasjonsbasert beslutningstaking. Derfor kan det være lovende å analysere slike datastrukturer ved bruk av moderne maskinlæringsteknikker for å øke forståelsen av pasientenes helseproblemer og for å gi klinikerne en bedre plattform for informasjonsbasert beslutningstaking. Sentrale krav til dette formålet inkluderer (a) tilstrekkelig nøyaktige prediksjoner og (b) modelltolkbarhet. Å oppnå begge aspektene samtidig er vanskelig, spesielt for datasett med få pasienter, noe som er vanlig for data i helsevesenet. I slike tilfeller må maskinlæringsmodeller håndtere matematisk underbestemte systemer og dette kan lett føre til at modellene overtilpasses treningsdataene. Variabelseleksjon er en viktig tilnærming for å håndtere dette ved å identifisere en undergruppe av informative variabler med hensyn til responsvariablen. Samtidig som variabelseleksjonsmetoder kan lede til økt prediktiv ytelse, fremmes modelltolkbarhet ved å identifisere et lavt antall relevante modellparametere. Dette kan gi bedre forståelse av de underliggende biologiske prosessene som fører til helseproblemer. Tolkbarhet krever at variabelseleksjonen er stabil, dvs. at små endringer i datasettet ikke fører til endringer i hvilke variabler som velges. Et konsept for å adressere ustabilitet er ensemblevariableseleksjon, dvs. prosessen med å gjenta variabelseleksjon flere ganger på en delmengde av prøvene i det originale datasett og aggregere resultater i en metamodell. Denne avhandlingen presenterer to tilnærminger for ensemblevariabelseleksjon, som er skreddersydd for høydimensjonale data i helsevesenet: "Repeated Elastic Net Technique for feature selection" (RENT) og "User-Guided Bayesian Framework for feature selection" (UBayFS). Mens RENT er datadrevet og bygger på elastic net-regulariserte modeller, er UBayFS et generelt rammeverk for ensembler som muliggjør inkludering av ekspertkunnskap i variabelseleksjonsprosessen gjennom forhåndsbestemte vekter og sidebegrensninger. En case-studie som modellerer overlevelsen av kreftpasienter sammenligner disse nye variabelseleksjonsmetodene og demonstrerer deres potensiale i klinisk praksis. Utover valg av enkelte variabler gjør UBayFS det også mulig å velge blokker eller grupper av variabler som representerer de ulike datakildene som ble nevnt over. Kvantifisering av viktigheten av variabelgrupper spiller en nøkkelrolle for forståelsen av hvorvidt datakildene er viktige for responsvariablen. Tilgang til slik informasjon kan føre til at bruken av menneskelige, tekniske og økonomiske ressurser kan forbedres dersom informasjonen integreres systematisk i planleggingen av pasientbehandlingen. Slik kan man redusere innsamling av ikke-informative variabler. Siden generaliseringen av viktighet av variabelgrupper ikke er triviell, undersøkes og sammenlignes også tilnærminger for rangering av viktigheten til disse variabelgruppene. Denne avhandlingen viser at høydimensjonale datasett fra flere datakilder fra det medisinske domenet effektivt kan håndteres ved bruk av variabelseleksjonmetodene som er presentert i avhandlingen. Eksperimentene viser at disse kan ha positiv en effekt på både prediktiv ytelse, stabilitet og tolkbarhet av resultatene. Bruken av disse variabelseleksjonsmetodene bærer et stort potensiale for bedre datadrevet beslutningsstøtte i klinisk praksis

ARDA: Automatic Relational Data Augmentation for Machine Learning

Automatic machine learning (\AML) is a family of techniques to automate the process of training predictive models, aiming to both improve performance and make machine learning more accessible. While many recent works have focused on aspects of the machine learning pipeline like model selection, hyperparameter tuning, and feature selection, relatively few works have focused on automatic data augmentation. Automatic data augmentation involves finding new features relevant to the user's predictive task with minimal ``human-in-the-loop'' involvement. We present \system, an end-to-end system that takes as input a dataset and a data repository, and outputs an augmented data set such that training a predictive model on this augmented dataset results in improved performance. Our system has two distinct components: (1) a framework to search and join data with the input data, based on various attributes of the input, and (2) an efficient feature selection algorithm that prunes out noisy or irrelevant features from the resulting join. We perform an extensive empirical evaluation of different system components and benchmark our feature selection algorithm on real-world datasets

Herd Routes: A Preventative IoT-Based System for Improving Female Pedestrian Safety on City Streets

Over two thirds of women of all ages in the UK have experienced some form of sexual harassment in a public space. Recent tragic incidents involving female pedestrians have highlighted some of the personal safety issues that women still face in cities today. There exist many popular location-based safety applications as a result of this; however, these applications tend to take a reactive approach where action is taken only after an incident has occurred. This paper proposes a preventative approach to the problem by creating safer public environments through societal incentivisation. The proposed system, called "Herd Routes", improves the safety of female pedestrians by generating busier pedestrian routes as a result of route incentivisation. A novel application of distributed ledgers is proposed to provide security and trust, a record of system users' locations and IDs, and a platform for token exchange. A proof-of-concept was developed using the simulation package SUMO (Simulation of Urban Mobility), and a smartphone app. was built in Android Studio so that pedestrian Hardware-in-the-Loop testing could be carried out to validate the technical feasibility and desirability of the system. With positive results from the initial testing of the proof-of-concept, further development could significantly contribute towards creating safer pedestrian routes through cities, and tackle the societal change that is required to improve female pedestrian safety in the long term

Machine learning methods for discriminating natural targets in seabed imagery

The research in this thesis concerns feature-based machine learning processes and methods for discriminating qualitative natural targets in seabed imagery. The applications considered, typically involve time-consuming manual processing stages in an industrial setting. An aim of the research is to facilitate a means of assisting human analysts by expediting the tedious interpretative tasks, using machine methods. Some novel approaches are devised and investigated for solving the application problems. These investigations are compartmentalised in four coherent case studies linked by common underlying technical themes and methods. The first study addresses pockmark discrimination in a digital bathymetry model. Manual identification and mapping of even a relatively small number of these landform objects is an expensive process. A novel, supervised machine learning approach to automating the task is presented. The process maps the boundaries of ≈ 2000 pockmarks in seconds - a task that would take days for a human analyst to complete. The second case study investigates different feature creation methods for automatically discriminating sidescan sonar image textures characteristic of Sabellaria spinulosa colonisation. Results from a comparison of several textural feature creation methods on sonar waterfall imagery show that Gabor filter banks yield some of the best results. A further empirical investigation into the filter bank features created on sonar mosaic imagery leads to the identification of a useful configuration and filter parameter ranges for discriminating the target textures in the imagery. Feature saliency estimation is a vital stage in the machine process. Case study three concerns distance measures for the evaluation and ranking of features on sonar imagery. Two novel consensus methods for creating a more robust ranking are proposed. Experimental results show that the consensus methods can improve robustness over a range of feature parameterisations and various seabed texture classification tasks. The final case study is more qualitative in nature and brings together a number of ideas, applied to the classification of target regions in real-world sonar mosaic imagery. A number of technical challenges arose and these were surmounted by devising a novel, hybrid unsupervised method. This fully automated machine approach was compared with a supervised approach in an application to the problem of image-based sediment type discrimination. The hybrid unsupervised method produces a plausible class map in a few minutes of processing time. It is concluded that the versatile, novel process should be generalisable to the discrimination of other subjective natural targets in real-world seabed imagery, such as Sabellaria textures and pockmarks (with appropriate features and feature tuning.) Further, the full automation of pockmark and Sabellaria discrimination is feasible within this framework

Active Learning for Text Classification

Text classification approaches are used extensively to solve real-world challenges. The success or failure of text classification systems hangs on the datasets used to train them, without a good dataset it is impossible to build a quality system. This thesis examines the applicability of active learning in text classification for the rapid and economical creation of labelled training data. Four main contributions are made in this thesis. First, we present two novel selection strategies to choose the most informative examples for manually labelling. One is an approach using an advanced aggregated confidence measurement instead of the direct output of classifiers to measure the confidence of the prediction and choose the examples with least confidence for querying. The other is a simple but effective exploration guided active learning selection strategy which uses only the notions of density and diversity, based on similarity, in its selection strategy. Second, we propose new methods of using deterministic clustering algorithms to help bootstrap the active learning process. We first illustrate the problems of using non-deterministic clustering for selecting initial training sets, showing how non-deterministic clustering methods can result in inconsistent behaviour in the active learning process. We then compare various deterministic clustering techniques and commonly used non-deterministic ones, and show that deterministic clustering algorithms are as good as non-deterministic clustering algorithms at selecting initial training examples for the active learning process. More importantly, we show that the use of deterministic approaches stabilises the active learning process. Our third direction is in the area of visualising the active learning process. We demonstrate the use of an existing visualisation technique in understanding active learning selection strategies to show that a better understanding of selection strategies can be achieved with the help of visualisation techniques. Finally, to evaluate the practicality and usefulness of active learning as a general dataset labelling methodology, it is desirable that actively labelled dataset can be reused more widely instead of being only limited to some particular classifier. We compare the reusability of popular active learning methods for text classification and identify the best classifiers to use in active learning for text classification. This thesis is concerned using active learning methods to label large unlabelled textual datasets. Our domain of interest is text classification, but most of the methods proposed are quite general and so are applicable to other domains having large collections of data with high dimensionality

Hammering towards QED

This paper surveys the emerging methods to automate reasoning over large libraries developed with formal proof assistants. We call these methods hammers. They give the authors of formal proofs a strong “one-stroke” tool for discharging difficult lemmas without the need for careful and detailed manual programming of proof search. The main ingredients underlying this approach are efficient automatic theorem provers that can cope with hundreds of axioms, suitable translations of the proof assistant’s logic to the logic of the automatic provers, heuristic and learning methods that select relevant facts from large libraries, and methods that reconstruct the automatically found proofs inside the proof assistants. We outline the history of these methods, explain the main issues and techniques, and show their strength on several large benchmarks. We also discuss the relation of this technology to the QED Manifesto and consider its implications for QED-like efforts.Blanchette’s Sledgehammer research was supported by the Deutsche Forschungs- gemeinschaft projects Quis Custodiet (grants NI 491/11-1 and NI 491/11-2) and Hardening the Hammer (grant NI 491/14-1). Kaliszyk is supported by the Austrian Science Fund (FWF) grant P26201. Sledgehammer was originally supported by the UK’s Engineering and Physical Sciences Research Council (grant GR/S57198/01). Urban’s work was supported by the Marie-Curie Outgoing International Fellowship project AUTOKNOMATH (grant MOIF-CT-2005-21875) and by the Netherlands Organisation for Scientific Research (NWO) project Knowledge-based Automated Reasoning (grant 612.001.208).This is the final published version. It first appeared at http://jfr.unibo.it/article/view/4593/5730?acceptCookies=1