Grey-Box Model: An ensemble approach for addressing semi-supervised classification problems

In this paper, we propose a novel and interpretable grey-box ensemble using a selflabeled approach for semi-supervised classification problems. The prospective greybox ensembles a more interpretable whitebox model with a black-box technique. This scheme could guide the comparatively data expensive white-box component with the results from the more accurate black-box part. We evaluate the proposal in an inductive learning setting showing good performance in partially labeled datasets

An ECMS-based powertrain control of a parallel hybrid electric forklift

n this paper we focus on the supervisory control problem of a parallel hybrid electric vehicle (HEV): minimize fuel consumption while ensuring self-sustaining State-of-Charge (SoC). We reapply the state of the art methodology by comparing optimal results of Dynamic Programming (DP) against a real-time control candidate. After careful selection, we opted for an Equivalent Consumption Minimization Strategy (ECMS) based approach for the following reasons: (i) results are quite remarkable with less than 5% fuel usage increase when compared to DP; (ii) simple and intuitive tuning of control parameters; (iii) readily usable for code generation (prototyping). Topics that distinguish this article from others in the literature include: (i) the usage of trapezoidal rule of integration implementing DP and ECMS; consequently, the offline simulation results are intended to be more precise and representative when compared against the more common, often used rectangular rule; (ii) a particular post-processing procedure of the recorded driving cycle data based on physical interpretation; it allows consistent offline simulations with quite high sampling period (in the order of seconds); (iii) tuning of control parameters in such a way that control system is robust towards new, unknown, unpredictable but closely resembling driving cycles. In particular, we focus on the supervisory control of a forklift truck. The real-time control is able to compute: (i) the power split (i.e. a balanced usage between an internal combustion engine and a supercapacitor); (ii) the drivetrain control (i.e. automatic gear shifting and clutching). Numerous numerical implementation issues are discussed along our presentation

Interpretable semisupervised classifier for predicting cancer stages

Machine learning techniques in medicine have been at the forefront addressing challenges such as diagnosis, prognosis prediction, or precision medicine. In this field, the data are sometimes abundant but comes from different data sources or lack assigned labels. The process of manually labeling these data when conforming to a curated dataset for supervised classification can be costly. Semisupervised classification offers a wide range of methods for leveraging unlabeled data when learning prediction models. However, these classifiers are commonly deep or ensemble learning structures that often result in black boxes. The requirement of interpretable models for medical settings led us to propose the self-labeling gray box classifier, which outperforms other semisupervised classifiers on benchmarking datasets while providing interpretability. In this chapter, we illustrate the applications of the self-labeling gray box on the omics and clinical datasets from the cancer genome atlas. We show that the self-labeling gray box is accurate in predicting cancer stages of rare cancers by leveraging the unlabeled instances from more common cancer types. We discuss insights, the features influencing prediction, and a global representation of the knowledge through decision trees or rule lists, which can aid clinicians and researchers

Collective intelligent wireless sensor networks

In this paper we apply the COllective INtelligence (COIN) framework ofWolpert et al. toWireless Sensor Networks (WSNs) with the aim to increase the autonomous lifetime of the network in a decentralized manner. COIN describes how selfish agents can learn to optimize their own performance, so that the performance of the global system is increased. WSNs are collections of densely deployed sensor nodes that gather environmental data, where the main challenges are the limited power supply of nodes and the need for decentralized control. To overcome these challenges, we make each sensor node adopt an algorithm to optimize its own energy efficiency, so that the energy efficiency of the whole system is increased. We introduce a new private utility function that will measure the performance of each agent and we show that nodes in WSNs are able develop an energy saving behaviour on their own, when using the COIN framework

Collective intelligent wireless sensor networks

In this paper we apply the COllective INtelligence (COIN) framework ofWolpert et al. toWireless Sensor Networks (WSNs) with the aim to increase the autonomous lifetime of the network in a decentralized manner. COIN describes how selfish agents can learn to optimize their own performance, so that the performance of the global system is increased. WSNs are collections of densely deployed sensor nodes that gather environmental data, where the main challenges are the limited power supply of nodes and the need for decentralized control. To overcome these challenges, we make each sensor node adopt an algorithm to optimize its own energy efficiency, so that the energy efficiency of the whole system is increased. We introduce a new private utility function that will measure the performance of each agent and we show that nodes in WSNs are able develop an energy saving behaviour on their own, when using the COIN framework

An interpretable semi-supervised classifier using rough sets for amended self-labeling

Semi-supervised classifiers combine labeled and unlabeled data during the learning phase in order to increase classifier's generalization capability. However, most successful semi-supervised classifiers involve complex ensemble structures and iterative algorithms which make it difficult to explain the outcome, thus behaving like black boxes. Furthermore, during an iterative self-labeling process, mistakes can be propagated if no amending procedure is used. In this paper, we build upon an interpretable self-labeling grey-box classifier that uses a black box to estimate the missing class labels and a white box to make the final predictions. We propose a Rough Set based approach for amending the self-labeling process. We compare its performance to the vanilla version of our self-labeling grey-box and the use of a confidence-based amending. In addition, we introduce some measures to quantify the interpretability of our model. The experimental results suggest that the proposed amending improves accuracy and interpretability of the self-labeling grey-box, thus leading to superior results when compared to state-of-the-art semi-supervised classifiers