LEVERAGING TEMPORAL SUBSEQUENCES FOR TIME-SERIES CLASSIFICATION

Abstract

Research on time-series classification has garnered importance among practitioners in the data mining community. A major reason behind the ever-increasing interest among data-miners is the plethora of time-series data available from a wide range of real-life domains. Temporal-ordered data from a variety of sensor-based domains such as wearable devices, smart homes, industrial monitoring, medical diagnosis, etc. provide classification challenges more akin to real-world scenarios. Thus, building more robust time-series classification models is imperative. One group of popular models focuses on identifying short discriminative temporal patterns (subsequences) from the time-series for classification. These temporal subsequences, known as shapelets, are local patterns that can be used to uniquely identify the target class of a time-series instance. In this dissertation, I explore two real-world challenges pertaining to shapelet based time-series classification models and provide solutions to mitigate those challenges. In the first challenge, the problem of cost-sensitive learning in time-series classification is explored. First, the problem of highly imbalanced time-series classification using shapelets is investigated. The current state-of-the-art approach learns generalized shapelets along with weights of the classification hyperplane via a classical cost-insensitive loss function. Cost-insensitive loss functions tend to treat different misclassification errors equally, and thus, models are usually biased towards examples of the majority class. In this research, the generalized shapelets learning framework is extended and a cost-sensitive learning model is proposed. Instead of incorporating the misclassification cost as prior knowledge, as was done by other published methods, a constrained optimization problem was formulated to learn the unknown misclassification costs along with the shapelets and their weights. Secondly, I focus on the problem of cost-sensitive early classification in time-series datasets. High false alarm rates in intensive care units (ICUs) cause desensitization among care providers, thus risking patients' lives. Providing early detection of true and false cardiac arrhythmia alarms can alert hospital personnel and avoid alarm fatigue. This will ensure hospital personnel can act only on true life-threatening alarms, hence improving efficiency in ICUs. Furthermore, suppressing false alarms cannot be an excuse to suppress true alarm detection rates. In this study, a cost-sensitive approach for false alarm suppression while keeping near perfect true alarm detection rates was investigated using a confidence estimate for shapelets matching. In the second challenge, the temporal dependencies among shapelets are explored. The existing shapelet-based methods for time-series classification assume that shapelets are independent of each other. However, they neglect temporal dependencies among pairs of shapelets, which are informative features that exist in many applications. Within this new framework, a scheme is explored to extract informative orders among shapelets by considering the time gap between pairs of shapelets. In this realm, two models are proposed, Pairwise Shapelet-Orders Discovery (PSOD) and Learning pairwise Orders and Shapelets (LOS), which extracts both informative shapelets and shapelet-orders and incorporates the shapelet-transformed space with shapelet-order space for time-series classification. The two proposed models are contrasting approaches in the time-series classification paradigm. The PSOD is a search-based greedy procedure to extract unique shapelets and identify orders among the selected shapelets. On the other hand, LOS is an optimization-based approach to extract shapelet-orders among learned generalized shapelets. However, in both the hypotheses, the extracted pairwise shapelet-orders could increase the confidence of the prediction and further improve the classification performance. The experimental results provide evidence that when considering shapelet-orders, classification accuracy is significantly improved on average over baseline methods. To the best of my knowledge, these are the first work that proposes formal methodologies to extract shapelet-orders and present augmented space of shapelets and shapelet-orders.