Evolving Spatially Aggregated Features from Satellite Imagery for Regional Modeling

Satellite imagery and remote sensing provide explanatory variables at relatively high resolutions for modeling geospatial phenomena, yet regional summaries are often desirable for analysis and actionable insight. In this paper, we propose a novel method of inducing spatial aggregations as a component of the machine learning process, yielding regional model features whose construction is driven by model prediction performance rather than prior assumptions. Our results demonstrate that Genetic Programming is particularly well suited to this type of feature construction because it can automatically synthesize appropriate aggregations, as well as better incorporate them into predictive models compared to other regression methods we tested. In our experiments we consider a specific problem instance and real-world dataset relevant to predicting snow properties in high-mountain Asia

Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space

As machine learning becomes an important part of many real world applications affecting human lives, new requirements, besides high predictive accuracy, become important. One important requirement is transparency, which has been associated with model interpretability. Many machine learning algorithms induce models difficult to interpret, named black box. Moreover, people have difficulty to trust models that cannot be explained. In particular for machine learning, many groups are investigating new methods able to explain black box models. These methods usually look inside the black models to explain their inner work. By doing so, they allow the interpretation of the decision making process used by black box models. Among the recently proposed model interpretation methods, there is a group, named local estimators, which are designed to explain how the label of particular instance is predicted. For such, they induce interpretable models on the neighborhood of the instance to be explained. Local estimators have been successfully used to explain specific predictions. Although they provide some degree of model interpretability, it is still not clear what is the best way to implement and apply them. Open questions include: how to best define the neighborhood of an instance? How to control the trade-off between the accuracy of the interpretation method and its interpretability? How to make the obtained solution robust to small variations on the instance to be explained? To answer to these questions, we propose and investigate two strategies: (i) using data instance properties to provide improved explanations, and (ii) making sure that the neighborhood of an instance is properly defined by taking the geometry of the domain of the feature space into account. We evaluate these strategies in a regression task and present experimental results that show that they can improve local explanations

A study of instance-based algorithms for supervised learning tasks : mathematical, empirical, and psychological evaluations

This dissertation introduces a framework for specifying instance-based algorithms that can solve supervised learning tasks. These algorithms input a sequence of instances and yield a partial concept description, which is represented by a set of stored instances and associated information. This description can be used to predict values for subsequently presented instances. The thesis of this framework is that extensional concept descriptions and lazy generalization strategies can support efficient supervised learning behavior.The instance-based learning framework consists of three components. The pre-processor component transforms an instance into a more palatable form for the performance component, which computes the instance's similarity with a set of stored instances and yields a prediction for its target value(s). Therefore, the similarity and prediction functions impose generalizations on the stored instances to inductively derive predictions. The learning component assesses the accuracy of these prediction(s) and updates partial concept descriptions to improve their predictive accuracy.This framework is evaluated in four ways. First, its generality is evaluated by mathematically determining the classes of symbolic concepts and numeric functions that can be closely approximated by IB_1, a simple algorithm specified by this framework. Second, this framework is empirically evaluated for its ability to specify algorithms that improve IB_1's learning efficiency. Significant efficiency improvements are obtained by instance-based algorithms that reduce storage requirements, tolerate noisy data, and learn domain-specific similarity functions respectively. Alternative component definitions for these algorithms are empirically analyzed in a set of five high-level parameter studies. Third, this framework is evaluated for its ability to specify psychologically plausible process models for categorization tasks. Results from subject experiments indicate a positive correlation between a models' ability to utilize attribute correlation information and its ability to explain psychological phenomena. Finally, this framework is evaluated for its ability to explain and relate a dozen prominent instance-based learning systems. The survey shows that this framework requires only slight modifications to fit these highly diverse systems. Relationships with edited nearest neighbor algorithms, case-based reasoners, and artificial neural networks are also described

Probabilistic Predictive Elicitation

Unlike the traditional machine learning approaches that rely solely on data, Bayesian machine learning models can utilize prior knowledge on the data generating process, for instance in form of information about plausible outcomes. More importantly, Bayesian machine learning models use the prior information as the base knowledge, on top of which the learning from observations is built on. The process of forming the prior distribution based on subjective probabilities is called prior elicitation, and that is the focus of this thesis. Although previous research has produced methods for prior elicitation, there has not been a general-purpose solution. Particularly, the methods introduced previously have focused on specific models. This has limited the applicability of prior elicitation, and in some cases, required the expert to have a deep understanding of different aspects of the Bayesian modelling. Additionally, the more general predictive elicitation methods in previous research have not accounted for the uncertainty regarding experts' judgements. This is important, since even the most accurate elicitation methods cannot remove all imprecision in expert judgements. Because of these reasons, prior elicitation has remained somewhat underrated and underused in the modern Bayesian workflow. This thesis provides a theoretical basis and validation of a novel prior elicitation method, which was first introduced by Hartmann et al. Particularly, this principled statistical framework called probabilistic predictive elicitation 1) makes prior elicitation independent on the specific structure of the probabilistic model, 2) handles complex models with many parameters and potentially multivariate priors, 3) fully accounts for uncertainty in experts' probabilistic judgements on the data, and 4) provides a formal quality measure indicating if the chosen predictive model is able to reproduce experts' probabilistic judgements. We extend the published work in multiple ways. First, we provide more thorough literature reviews on different prior elicitation approaches as well as on methods for the expert elicitation. Second, we continue the discussion about technicalities, implementation and applications of the proposed methodology. Third, we report two unpublished experiments using the proposed methodology. In addition, we discuss the methodology in the context of the modern Bayesian workflow