197,593 research outputs found
A Historical Context for Data Streams
Machine learning from data streams is an active and growing research area.
Research on learning from streaming data typically makes strict assumptions
linked to computational resource constraints, including requirements for stream
mining algorithms to inspect each instance not more than once and be ready to
give a prediction at any time. Here we review the historical context of data
streams research placing the common assumptions used in machine learning over
data streams in their historical context.Comment: 9 page
A review on data stream classification
At this present time, the significance of data streams cannot be denied as many researchers have placed their focus on the research areas of databases, statistics, and computer science. In fact, data streams refer to some data points sequences that are found in order with the potential to be non-binding, which is generated from the process of generating information in a manner that is not stationary. As such the typical tasks of searching data have been linked to streams of data that are inclusive of clustering, classification, and repeated mining of pattern. This paper presents several data stream clustering approaches, which are based on density, besides attempting to comprehend the function of the related algorithms; both semi-supervised and active learning, along with reviews of a number of recent studies
A survey on online active learning
Online active learning is a paradigm in machine learning that aims to select
the most informative data points to label from a data stream. The problem of
minimizing the cost associated with collecting labeled observations has gained
a lot of attention in recent years, particularly in real-world applications
where data is only available in an unlabeled form. Annotating each observation
can be time-consuming and costly, making it difficult to obtain large amounts
of labeled data. To overcome this issue, many active learning strategies have
been proposed in the last decades, aiming to select the most informative
observations for labeling in order to improve the performance of machine
learning models. These approaches can be broadly divided into two categories:
static pool-based and stream-based active learning. Pool-based active learning
involves selecting a subset of observations from a closed pool of unlabeled
data, and it has been the focus of many surveys and literature reviews.
However, the growing availability of data streams has led to an increase in the
number of approaches that focus on online active learning, which involves
continuously selecting and labeling observations as they arrive in a stream.
This work aims to provide an overview of the most recently proposed approaches
for selecting the most informative observations from data streams in the
context of online active learning. We review the various techniques that have
been proposed and discuss their strengths and limitations, as well as the
challenges and opportunities that exist in this area of research. Our review
aims to provide a comprehensive and up-to-date overview of the field and to
highlight directions for future work
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
Online Active Learning for Human Activity Recognition from Sensory Data Streams
Human activity recognition (HAR) is highly relevant to many real-world do- mains like safety, security, and in particular healthcare. The current machine learning technology of HAR is highly human-dependent which makes it costly and unreliable in non-stationary environment. Existing HAR algorithms assume that training data is collected and annotated by human a prior to the training phase. Furthermore, the data is assumed to exhibit the true characteristics of the underlying distribution. In this paper, we propose a new autonomous approach that consists of novel algorithms. In particular, we adopt active learning (AL) strategy to selectively query the user/resident about the label of particular activities in order to improve the model accuracy. This strategy helps overcome the challenge of labelling sequential data with time dependency which is highly time-consuming and difficult. Because of the changes that may affect the way activities are performed, we regard sensor data as a stream and human activity learning as an online continuous process. In such process the leaner can adapt to changes, incorporate novel activities and discard obsolete ones. To this extent, we propose a novel semi-supervised classifier (OSC) that works together with a novel Bayesian stream-based active learning (BSAL). Because of the changes in the sensor layouts across different houses' settings, we use Conditional Re-stricted Boltzmann Machine (CRBM) to handle the features engineering issue by learning the features regardless of the environment settings. CRBM is then applied to extract low-level features from unlabelled raw high-dimensional activity input. The resulting approach will then tackle the challenges of activity recognition using a three-module architecture composed of a feature extractor (CRBM), an online semi-supervised classifier (OSC) equipped with BSAL. CRBM-BSAL-OSC allows completely autonomous learning that adjusts to the environment setting, explores the changes and adapt to them. The paper provides the theoretical details of the proposed approach as well as an extensive empirical study to evaluate the performance of the approach. we propose a novel semi-supervised classifier (OSC) that works together with a novel Bayesian stream-based active learning (BSAL). Because of the changes in the sensor layouts across di erent houses' settings, we use Conditional Re
Clustering based active learning for evolving data streams
Data labeling is an expensive and time-consuming task. Choosing which labels to use is increasingly becoming important. In the active learning setting, a classifier is trained by asking for labels for only a small fraction of all instances. While many works exist that deal with this issue in non-streaming scenarios, few works exist in the data stream setting. In this paper we propose a new active learning approach for evolving data streams based on a pre-clustering step, for selecting the most informative instances for labeling. We consider a batch incremental setting: when a new batch arrives, first we cluster the examples, and then, we select the best instances to train the learner. The clustering approach allows to cover the whole data space avoiding to oversample examples from only few areas. We compare our method w.r.t. state of the art active learning strategies over real datasets. The results highlight the improvement in performance of our proposal. Experiments on parameter sensitivity are also reported
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