5,769 research outputs found
Classifiers With a Reject Option for Early Time-Series Classification
Early classification of time-series data in a dynamic environment is a
challenging problem of great importance in signal processing. This paper
proposes a classifier architecture with a reject option capable of online
decision making without the need to wait for the entire time series signal to
be present. The main idea is to classify an odor/gas signal with an acceptable
accuracy as early as possible. Instead of using posterior probability of a
classifier, the proposed method uses the "agreement" of an ensemble to decide
whether to accept or reject the candidate label. The introduced algorithm is
applied to the bio-chemistry problem of odor classification to build a novel
Electronic-Nose called Forefront-Nose. Experimental results on wind tunnel
test-bed facility confirms the robustness of the forefront-nose compared to the
standard classifiers from both earliness and recognition perspectives
Supervised Classification: Quite a Brief Overview
The original problem of supervised classification considers the task of
automatically assigning objects to their respective classes on the basis of
numerical measurements derived from these objects. Classifiers are the tools
that implement the actual functional mapping from these measurements---also
called features or inputs---to the so-called class label---or output. The
fields of pattern recognition and machine learning study ways of constructing
such classifiers. The main idea behind supervised methods is that of learning
from examples: given a number of example input-output relations, to what extent
can the general mapping be learned that takes any new and unseen feature vector
to its correct class? This chapter provides a basic introduction to the
underlying ideas of how to come to a supervised classification problem. In
addition, it provides an overview of some specific classification techniques,
delves into the issues of object representation and classifier evaluation, and
(very) briefly covers some variations on the basic supervised classification
task that may also be of interest to the practitioner
Reliable Multi-label Classification: Prediction with Partial Abstention
In contrast to conventional (single-label) classification, the setting of
multilabel classification (MLC) allows an instance to belong to several classes
simultaneously. Thus, instead of selecting a single class label, predictions
take the form of a subset of all labels. In this paper, we study an extension
of the setting of MLC, in which the learner is allowed to partially abstain
from a prediction, that is, to deliver predictions on some but not necessarily
all class labels. We propose a formalization of MLC with abstention in terms of
a generalized loss minimization problem and present first results for the case
of the Hamming loss, rank loss, and F-measure, both theoretical and
experimental.Comment: 19 pages, 12 figure
Classification of Time-Series Images Using Deep Convolutional Neural Networks
Convolutional Neural Networks (CNN) has achieved a great success in image
recognition task by automatically learning a hierarchical feature
representation from raw data. While the majority of Time-Series Classification
(TSC) literature is focused on 1D signals, this paper uses Recurrence Plots
(RP) to transform time-series into 2D texture images and then take advantage of
the deep CNN classifier. Image representation of time-series introduces
different feature types that are not available for 1D signals, and therefore
TSC can be treated as texture image recognition task. CNN model also allows
learning different levels of representations together with a classifier,
jointly and automatically. Therefore, using RP and CNN in a unified framework
is expected to boost the recognition rate of TSC. Experimental results on the
UCR time-series classification archive demonstrate competitive accuracy of the
proposed approach, compared not only to the existing deep architectures, but
also to the state-of-the art TSC algorithms.Comment: The 10th International Conference on Machine Vision (ICMV 2017
Reducing Pulse Oximetry False Alarms Without Missing Life-Threatening Events
Alarm fatigue has been increasingly recognized as one of the most significant problems in the hospital environment. One of the major causes is the excessive number of false physiologic monitor alarms. An underlying problem is the inefficient traditional threshold alarm system for physiologic parameters such as low blood oxygen saturation (SpO2). In this paper, we propose a robust classification procedure based on the AdaBoost algorithm with reject option that can identify and silence false SpO2 alarms, while ensuring zero misclassified clinically significant alarms. Alarms and vital signs related to SpO2 such as heart rate and pulse rate, within monitoring interval are extracted into different numerical features for the classifier. We propose a variant of AdaBoost with reject option by allowing a third decision (i.e., reject) expressing doubt. Weighted outputs of each weak classifier are input to a softmax function optimizing to satisfy a desired false negative rate upper bound while minimizing false positive rate and indecision rate. We evaluate the proposed classifier using a dataset collected from 100 hospitalized children at Children\u27s Hospital of Philadelphia and show that the classifier can silence 23.12% of false SpO2 alarms without missing any clinically significant alarms
Multi-Stage Classifier Design
In many classification systems, sensing modalities have different acquisition
costs. It is often {\it unnecessary} to use every modality to classify a
majority of examples. We study a multi-stage system in a prediction time cost
reduction setting, where the full data is available for training, but for a
test example, measurements in a new modality can be acquired at each stage for
an additional cost. We seek decision rules to reduce the average measurement
acquisition cost. We formulate an empirical risk minimization problem (ERM) for
a multi-stage reject classifier, wherein the stage classifier either
classifies a sample using only the measurements acquired so far or rejects it
to the next stage where more attributes can be acquired for a cost. To solve
the ERM problem, we show that the optimal reject classifier at each stage is a
combination of two binary classifiers, one biased towards positive examples and
the other biased towards negative examples. We use this parameterization to
construct stage-by-stage global surrogate risk, develop an iterative algorithm
in the boosting framework and present convergence and generalization results.
We test our work on synthetic, medical and explosives detection datasets. Our
results demonstrate that substantial cost reduction without a significant
sacrifice in accuracy is achievable
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