Ada-WHIPS: explaining AdaBoost classification with applications in the health sciences

Background Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients’ disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and to supplement their own expertise. Yet, CAD systems might be based on black box machine learning models and high dimensional data sources such as electronic health records, magnetic resonance imaging scans, cardiotocograms, etc. These foundations make interpretation and explanation of the CAD advice very challenging. This challenge is recognised throughout the machine learning research community. eXplainable Artificial Intelligence (XAI) is emerging as one of the most important research areas of recent years because it addresses the interpretability and trust concerns of critical decision makers, including those in clinical and medical practice. Methods In this work, we focus on AdaBoost, a black box model that has been widely adopted in the CAD literature. We address the challenge – to explain AdaBoost classification – with a novel algorithm that extracts simple, logical rules from AdaBoost models. Our algorithm, Adaptive-Weighted High Importance Path Snippets (Ada-WHIPS), makes use of AdaBoost’s adaptive classifier weights. Using a novel formulation, Ada-WHIPS uniquely redistributes the weights among individual decision nodes of the internal decision trees of the AdaBoost model. Then, a simple heuristic search of the weighted nodes finds a single rule that dominated the model’s decision. We compare the explanations generated by our novel approach with the state of the art in an experimental study. We evaluate the derived explanations with simple statistical tests of well-known quality measures, precision and coverage, and a novel measure stability that is better suited to the XAI setting. Results Experiments on 9 CAD-related data sets showed that Ada-WHIPS explanations consistently generalise better (mean coverage 15%-68%) than the state of the art while remaining competitive for specificity (mean precision 80%-99%). A very small trade-off in specificity is shown to guard against over-fitting which is a known problem in the state of the art methods. Conclusions The experimental results demonstrate the benefits of using our novel algorithm for explaining CAD AdaBoost classifiers widely found in the literature. Our tightly coupled, AdaBoost-specific approach outperforms model-agnostic explanation methods and should be considered by practitioners looking for an XAI solution for this class of models

CHIRPS: Explaining random forest classification

Modern machine learning methods typically produce “black box” models that are opaque to interpretation. Yet, their demand has been increasing in the Human-in-the-Loop pro-cesses, that is, those processes that require a human agent to verify, approve or reason about the automated decisions before they can be applied. To facilitate this interpretation, we propose Collection of High Importance Random Path Snippets (CHIRPS); a novel algorithm for explaining random forest classification per data instance. CHIRPS extracts a decision path from each tree in the forest that contributes to the majority classification, and then uses frequent pattern mining to identify the most commonly occurring split conditions. Then a simple, conjunctive form rule is constructed where the antecedent terms are derived from the attributes that had the most influence on the classification. This rule is returned alongside estimates of the rule’s precision and coverage on the training data along with counter-factual details. An experimental study involving nine data sets shows that classification rules returned by CHIRPS have a precision at least as high as the state of the art when evaluated on unseen data (0.91–0.99) and offer a much greater coverage (0.04–0.54). Furthermore, CHIRPS uniquely controls against under- and over-fitting solutions by maximising novel objective functions that are better suited to the local (per instance) explanation setting

Improving the generalisation of genetic programming models with evaluation time and asynchronous parallel computing

In genetic programming (GP), controlling complexity often means reducing the size of evolved expressions. However, previous studies show that size reduction may not avoid model overfitting. Therefore, in this study, we use the evaluation time --- the computational time required to evaluate a GP model on data --- as the estimate of model complexity. The evaluation time depends not only on the size of evolved expressions but also their composition, thus acting as a more nuanced measure of model complexity than size alone. To constrain complexity using this measure of complexity, we employed an explicit control technique and a method that creates a race condition. We used a hybridisation of GP and multiple linear regression (MLRGP) that discovers useful features to boost training performance in our experiments. The improved training increases the chances of overfitting and facilitates a study of how managing complexity with evaluation time can address overfitting. Also, MLRGP allows us to observe the relationship between evaluation time and the number of features in a model. The results show that constraining evaluation time of MLRGP leads to better generalisation than both plain MLRGP and with an effective bloat-control

Time Control or Size Control? Reducing Complexity and Improving Accuracy of Genetic Programming Models

Complexity of evolving models in genetic programming (GP) can impact both the quality of the models and the evolutionary search. While previous studies have proposed several notions of GP model complexity, the size of a GP model is by far the most researched measure of model complexity. However, previous studies have also shown that controlling the size does not automatically improve the accuracy of GP models, especially the accuracy on out of sample (test) data. Furthermore, size does not represent the functional composition of a model, which is often related to its accuracy on test data. In this study, we explore the {\em evaluation time} of GP models as a measure of their complexity; we define the evaluation time as the time taken to evaluate a model over some data. We demonstrate that the evaluation time reflects both a model’s size and its composition; also, we show how to measure the evaluation time reliably. To validate our proposal, we leverage four well-known methods to size-control but to control evaluation times instead of the tree sizes; we thus compare size-control with time-control. The results show that time-control with a nuanced notion of complexity produces more accurate models on 17 out of 20 problem scenarios. Even when the models have slightly greater times and sizes, time-control counterbalances via superior accuracy on both training and test data. The paper also argues that time-control can differentiate functional complexity even better in an identically-sized population. To facilitate this, the paper proposes Fixed Length Initialisation (FLI) that creates an identically-sized but functionally-diverse population. The results show that while FLI particularly suits time-control, it also generally improves the performance of size-control. Overall, the paper poses evaluation-time as a viable alternative to tree sizes to measure complexity in GP

Principled Evolutionary Algorithm Search Operator Design and the Kernel Trick

Configuring an Evolutionary Algorithm (EA) can be a haphazard and inefficient process. An EA practitioner may have to choose between a plethora of search operator types and other parameter settings. In contrast, the goal of EA principled design is a more streamlined and systematic design methodology, which first seeks to better understand the problem domain, and only then uses such acquired insights to guide the choice of parameters and operators

Principled Evolutionary Algorithm Design and the Kernel Trick

We introduce a new approach to the principled design of evolutionary algorithms (EAs) based on kernel methods. We demonstrate how kernel functions, which capture useful problem domain knowledge, can be used to directly construct EA search operators. We test two kernel search operators on a suite of four challenging combinatorial optimization problem domains. These novel kernel search operators exhibit superior performance to some traditional EA search operators

gbt-HIPS: Explaining the Classifications of Gradient Boosted Tree Ensembles

This research presents Gradient Boosted Tree High Importance Path Snippets (gbt-HIPS), a novel, heuristic method for explaining gradient boosted tree (GBT) classification models by extracting a single classification rule (CR) from the ensemble of decision trees that make up the GBT model. This CR contains the most statistically important boundary values of the input space as antecedent terms. The CR represents a hyper-rectangle of the input space inside which the GBT model is, very reliably, classifying all instances with the same class label as the explanandum instance. In a benchmark test using nine data sets and five competing state-of-the-art methods, gbt-HIPS offered the best trade-off between coverage (0.16–0.75) and precision (0.85–0.98). Unlike competing methods, gbt-HIPS is also demonstrably guarded against under- and over-fitting. A further distinguishing feature of our method is that, unlike much prior work, our explanations also provide counterfactual detail in accordance with widely accepted recommendations for what makes a good explanation

A New Wave: A Dynamic Approach to Genetic Programming

Wave is a novel form of semantic genetic programming which operates by optimising the residual errors of a succession of short genetic programming runs, and then producing a cumulative solution. These short genetic programming runs are called periods, and they have heterogeneous parameters. In this paper we leverage the potential of Wave's heterogeneity to simulate a dynamic evolutionary environment by incorporating self adaptive parameters together with an innovative approach to population renewal. We conduct an empirical study comparing this new approach with multiple linear regression~(MLR) as well as several evolutionary computation~(EC) methods including the well known geometric semantic genetic programming~(GSGP) together with several other optimised Wave techniques. The results of our investigation show that the dynamic Wave algorithm delivers consistently equal or better performance than Standard GP (both with or without linear scaling), achieves testing fitness equal or better than multiple linear regression, and performs significantly better than GSGP on five of the six problems studied