Online Tool Condition Monitoring Based on Parsimonious Ensemble+

Accurate diagnosis of tool wear in metal turning process remains an open challenge for both scientists and industrial practitioners because of inhomogeneities in workpiece material, nonstationary machining settings to suit production requirements, and nonlinear relations between measured variables and tool wear. Common methodologies for tool condition monitoring still rely on batch approaches which cannot cope with a fast sampling rate of metal cutting process. Furthermore they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online tool condition monitoring approach based on Parsimonious Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly flexible principle where both ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. The paper presents advancement of a newly developed ensemble learning algorithm, pENsemble+, where online active learning scenario is incorporated to reduce operator labelling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilising real-world manufacturing data streams and comparisons with well known algorithms were carried out. Furthermore, the efficacy of pENsemble was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic

Analysis of classifiers' robustness to adversarial perturbations

The goal of this paper is to analyze an intriguing phenomenon recently discovered in deep networks, namely their instability to adversarial perturbations (Szegedy et. al., 2014). We provide a theoretical framework for analyzing the robustness of classifiers to adversarial perturbations, and show fundamental upper bounds on the robustness of classifiers. Specifically, we establish a general upper bound on the robustness of classifiers to adversarial perturbations, and then illustrate the obtained upper bound on the families of linear and quadratic classifiers. In both cases, our upper bound depends on a distinguishability measure that captures the notion of difficulty of the classification task. Our results for both classes imply that in tasks involving small distinguishability, no classifier in the considered set will be robust to adversarial perturbations, even if a good accuracy is achieved. Our theoretical framework moreover suggests that the phenomenon of adversarial instability is due to the low flexibility of classifiers, compared to the difficulty of the classification task (captured by the distinguishability). Moreover, we show the existence of a clear distinction between the robustness of a classifier to random noise and its robustness to adversarial perturbations. Specifically, the former is shown to be larger than the latter by a factor that is proportional to \sqrt{d} (with d being the signal dimension) for linear classifiers. This result gives a theoretical explanation for the discrepancy between the two robustness properties in high dimensional problems, which was empirically observed in the context of neural networks. To the best of our knowledge, our results provide the first theoretical work that addresses the phenomenon of adversarial instability recently observed for deep networks. Our analysis is complemented by experimental results on controlled and real-world data

Multi-Sensor Event Detection using Shape Histograms

Vehicular sensor data consists of multiple time-series arising from a number of sensors. Using such multi-sensor data we would like to detect occurrences of specific events that vehicles encounter, e.g., corresponding to particular maneuvers that a vehicle makes or conditions that it encounters. Events are characterized by similar waveform patterns re-appearing within one or more sensors. Further such patterns can be of variable duration. In this work, we propose a method for detecting such events in time-series data using a novel feature descriptor motivated by similar ideas in image processing. We define the shape histogram: a constant dimension descriptor that nevertheless captures patterns of variable duration. We demonstrate the efficacy of using shape histograms as features to detect events in an SVM-based, multi-sensor, supervised learning scenario, i.e., multiple time-series are used to detect an event. We present results on real-life vehicular sensor data and show that our technique performs better than available pattern detection implementations on our data, and that it can also be used to combine features from multiple sensors resulting in better accuracy than using any single sensor. Since previous work on pattern detection in time-series has been in the single series context, we also present results using our technique on multiple standard time-series datasets and show that it is the most versatile in terms of how it ranks compared to other published results

Machine learning on a budget

Thesis (Ph.D.)--Boston UniversityIn a typical discriminative learning setting, a set of labeled training examples is given, and the goal is to learn a decision rule that accurately classifies (or labels) unseen test examples. Much of machine learning research has focused on improving accuracy, but more recently costs of learning and decision making are becoming more important. Such costs arise both during training and testing. Labeling data for training is often an expensive process. During testing, acquiring or processing measurements for every decision is also costly. This work deals with two problems: how to reduce the amount of labeled data during training, and how to minimize measurements cost in making decisions during testing, while maintaining system accuracy. The first part falls into an area known as active learning. It deals with the problem of selecting a small subset of examples to label, from a pool of unlabeled data, for training a good classifier. This problem is relevant in many applications where a large collection of unlabeled data is readily available but to label an instance requires using an expensive expert (a radiologist annotating a medical image). We study active learning in the boosting framework. We develop a practical algorithm that labels examples to maximally reduce the space of feasible classifiers. We show that, under certain assumptions, our strategy achieves the generalization error performance of a system trained on the entire data set while only selecting logarithmically many samples to label. In the second part, we study sequential classifiers under budget constraints. In many systems, such as medical diagnosis and homeland security, sensors have varying acquisition costs, and these costs account for delay, throughput or monetary value. While some decisions require all measurements, it is often unnecessary to use every modality to classify every example. So the problem is to learn a system that, for every decision, sequentially selects sensors to meet a measurement budget while minimizing classification error. Initially, we study the case where the sensor order in which measurement are acquired is given. For every instance, our system has to decide whether to seek more measurements from the next sensor or to terminate by classifying based on the available information. We use Bayesian analysis of this problem to construct a novel multi-stage empirical risk objective and directly learn sequential decision functions from training data. We provide practical algorithms for binary and multi-class settings and derive generalization error guarantees. We compare our approach to alternative strategies on real world data. In the last section, we explore a decision system when the order of sensors is no longer fixed. We investigate how to combine ideas from reinforcement and imitation learning with empirical risk minimization to learn a dynamic sensor selection policy