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 measurestabilitythat 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.

Genetic programming (GP) coarsely models natural evolution to evolve computer programs. Unlike in nature, where individuals can often improve their fitness through lifetime experience, the fitness of GP individuals generally does not change during their lifetime, and there is usually no opportunity to pass on acquired knowledge. This paper introduces the Chameleon system to address this discrepancy and augment GP with lifetime learning by adding a simple local search that operates by tuning the internal nodes of individuals. Although not the first attempt to combine local search with GP, its simplicity means that it is easy to understand and cheap to implement. A simple cache is added which leverages the local search to reduce the tuning cost to a small fraction of the expected cost, and we provide a theoretical upper limit on the maximum tuning expense given the average tree size of the population and show that this limit grows very conservatively as the average tree size of the population increases. We show that Chameleon uses available genetic material more efficiently by exploring more actively than with standard GP, and demonstrate that not only does Chameleon outperform standard GP (on both training and test data) over a number of symbolic regression type problems, it does so by producing smaller individuals and it works harmoniously with two other well-known extensions to GP, namely, linear scaling and a diversity-promoting tournament selection method.

Historically, the quality of a solution in Genetic Programming (GP) was often assessed based on its performance on a given training sample. However, in Machine Learning, we are more interested in achieving reliable estimates of the quality of the evolving individuals on unseen data. In this paper, we propose to simulate the effect of unseen data during training without actually using any additional data. We do this by employing a technique called bootstrapping that repeatedly re-samples with replacement from the training data and helps estimate sensitivity of the individual in question to small variations across these re-sampled data sets. We minimise this sensitivity, as measured by the Bootstrap Standard Error, together with the training error, in an effort to evolve models that generalise better to the unseen data. We evaluate the proposed technique on four binary classification problems and compare with a standard GP approach. The results show that for the problems undertaken, the proposed method not only generalises significantly better than standard GP while the training performance improves, but also demonstrates a strong side effect of containing the tree sizes.

Early detection of emerging research trends could potentially revolutionise the way research is done. For this reason, trend analysis has become an area of paramount importance in academia and industry. This is due to the significant implications for research funding and public policy. The literature presents several emerging approaches to detecting new research trends. Most of these approaches rely mainly on citation counting. While citations have been widely used as indicators of emerging research topics, they suffer from some limitations. For instance, citations can take months to years to progress and then to reveal trends. Furthermore, they fail to dig into paper content. To overcome this problem, we introduce Leap2Trend, a novel approach to instant detection of research trends. Leap2Trend relies on temporal word embeddings ( word2vec) to track the dynamics of similarities between pairs of keywords, their rankings and respective uprankings (ascents) over time. We applied Leap2Trend to two scientific corpora on different research areas, namely computer science and bioinformatics and we evaluated it against two gold standards Google Trends hits and Google Scholar citations. The obtained results reveal the effectiveness of our approach to detect trends with more than 80% accuracy and 90% precision in some cases. Such significant findings evidence the utility of our Leap2Trend approach for tracking and detecting emerging research trends instantly.