Genetic Adversarial Training of Decision Trees

We put forward a novel learning methodology for ensembles of decision trees based on a genetic algorithm which is able to train a decision tree for maximizing both its accuracy and its robustness to adversarial perturbations. This learning algorithm internally leverages a complete formal verification technique for robustness properties of decision trees based on abstract interpretation, a well known static program analysis technique. We implemented this genetic adversarial training algorithm in a tool called Meta-Silvae (MS) and we experimentally evaluated it on some reference datasets used in adversarial training. The experimental results show that MS is able to train robust models that compete with and often improve on the current state-of-the-art of adversarial training of decision trees while being much more compact and therefore interpretable and efficient tree models

Real-Time Induction Motor Health Index Prediction in A Petrochemical Plant using Machine Learning

This paper presents real-time health prediction of induction motors (IMs) utilised in a petrochemical plant through the application of intelligent sensors and machine learning (ML) models. At present, maintenance engineers of the company implement time-based and condition-based maintenance techniques in periodically examining and diagnosing the health of IMs which results in sporadic breakdowns of IMs. Such breakdowns sometimes force the entire production process to stop for emergency maintenance resulting in a huge loss in the company’s revenue. Hence, top management decides to switch the operational practice to real-time predictive maintenance instead. Intelligent sensors are installed on IMs to collect necessary information related to their working statuses. ML exploits the real-time information received from intelligent sensors to flag abnormalities of mechanical or electrical components of IMs before potential failures are reached. Four ML models are investigated to evaluate which one is the best, i.e. Artificial Neural Network (ANN), Particle Swarm Optimization (PSO), Gradient Boosting Tree (GBT) and Random Forest (RF). Standard performance metrics are used to compare the relative effectiveness among different ML models including Precision, Recall, Accuracy, F1-score, and AUC-ROC curve. The results reveal that PSO not only obtains the highest average weighted Accuracy but also can differentiate the statuses (Class 0 – Class 3) of the IM more correctly than other counterpart models

A Review of Formal Methods applied to Machine Learning

We review state-of-the-art formal methods applied to the emerging field of the verification of machine learning systems. Formal methods can provide rigorous correctness guarantees on hardware and software systems. Thanks to the availability of mature tools, their use is well established in the industry, and in particular to check safety-critical applications as they undergo a stringent certification process. As machine learning is becoming more popular, machine-learned components are now considered for inclusion in critical systems. This raises the question of their safety and their verification. Yet, established formal methods are limited to classic, i.e. non machine-learned software. Applying formal methods to verify systems that include machine learning has only been considered recently and poses novel challenges in soundness, precision, and scalability. We first recall established formal methods and their current use in an exemplar safety-critical field, avionic software, with a focus on abstract interpretation based techniques as they provide a high level of scalability. This provides a golden standard and sets high expectations for machine learning verification. We then provide a comprehensive and detailed review of the formal methods developed so far for machine learning, highlighting their strengths and limitations. The large majority of them verify trained neural networks and employ either SMT, optimization, or abstract interpretation techniques. We also discuss methods for support vector machines and decision tree ensembles, as well as methods targeting training and data preparation, which are critical but often neglected aspects of machine learning. Finally, we offer perspectives for future research directions towards the formal verification of machine learning systems

Adversarial training of gradient-boosted decision trees

Adversarial training is a prominent approach to make machine learning (ML) models resilient to adversarial examples. Unfortunately, such approach assumes the use of differentiable learning models, hence it cannot be applied to relevant ML techniques, such as ensembles of decision trees. In this paper, we generalize adversarial training to gradient-boosted decision trees (GBDTs). Our experiments show that the performance of classifiers based on existing learning techniques either sharply decreases upon attack or is unsatisfactory in absence of attacks, while adversarial training provides a very good trade-off between resiliency to attacks and accuracy in the unattacked setting