scholarly journals A reduced labeled samples (RLS) framework for classification of imbalanced concept-drifting streaming data.

2016 ◽  
Author(s):  
Elaheh Arabmakki
Keyword(s):  
Streaming Data

scholarly journals Deep learning framework for handling concept drift and class imbalanced complex decision-making on streaming data

2021 ◽  
Author(s):  
S. Priya ◽  
R. Annie Uthra
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Concept Drift ◽  
Class Imbalance ◽  
Streaming Data ◽  
Superior Performance ◽  
Data Streaming ◽  
Minority Class ◽  
Concept Drift Detection

AbstractIn present times, data science become popular to support and improve decision-making process. Due to the accessibility of a wide application perspective of data streaming, class imbalance and concept drifting become crucial learning problems. The advent of deep learning (DL) models finds useful for the classification of concept drift in data streaming applications. This paper presents an effective class imbalance with concept drift detection (CIDD) using Adadelta optimizer-based deep neural networks (ADODNN), named CIDD-ADODNN model for the classification of highly imbalanced streaming data. The presented model involves four processes namely preprocessing, class imbalance handling, concept drift detection, and classification. The proposed model uses adaptive synthetic (ADASYN) technique for handling class imbalance data, which utilizes a weighted distribution for diverse minority class examples based on the level of difficulty in learning. Next, a drift detection technique called adaptive sliding window (ADWIN) is employed to detect the existence of the concept drift. Besides, ADODNN model is utilized for the classification processes. For increasing the classifier performance of the DNN model, ADO-based hyperparameter tuning process takes place to determine the optimal parameters of the DNN model. The performance of the presented model is evaluated using three streaming datasets namely intrusion detection (NSL KDDCup) dataset, Spam dataset, and Chess dataset. A detailed comparative results analysis takes place and the simulation results verified the superior performance of the presented model by obtaining a maximum accuracy of 0.9592, 0.9320, and 0.7646 on the applied KDDCup, Spam, and Chess dataset, respectively.

scholarly journals Incremental Learning for Classification of Unstructured Data Using Extreme Learning Machine

Algorithms ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 158 ◽  
Author(s):  
Sathya Madhusudhanan ◽  
Suresh Jaganathan ◽  
Jayashree L S
Keyword(s):  
Extreme Learning Machine ◽  
Incremental Learning ◽  
Streaming Data ◽  
Unstructured Data ◽  
Feed Forward Neural Network ◽  
Training Time ◽  
Class Labels ◽  
Learning Machine ◽  
Over Time

Unstructured data are irregular information with no predefined data model. Streaming data which constantly arrives over time is unstructured, and classifying these data is a tedious task as they lack class labels and get accumulated over time. As the data keeps growing, it becomes difficult to train and create a model from scratch each time. Incremental learning, a self-adaptive algorithm uses the previously learned model information, then learns and accommodates new information from the newly arrived data providing a new model, which avoids the retraining. The incrementally learned knowledge helps to classify the unstructured data. In this paper, we propose a framework CUIL (Classification of Unstructured data using Incremental Learning) which clusters the metadata, assigns a label for each cluster and then creates a model using Extreme Learning Machine (ELM), a feed-forward neural network, incrementally for each batch of data arrived. The proposed framework trains the batches separately, reducing the memory resources, training time significantly and is tested with metadata created for the standard image datasets like MNIST, STL-10, CIFAR-10, Caltech101, and Caltech256. Based on the tabulated results, our proposed work proves to show greater accuracy and efficiency.

Multi-window based ensemble learning for classification of imbalanced streaming data

2017 ◽  
Vol 20 (6) ◽  
pp. 1507-1525 ◽  
Author(s):  
Hu Li ◽  
Ye Wang ◽  
Hua Wang ◽  
Bin Zhou
Keyword(s):  
Ensemble Learning ◽  
Streaming Data

scholarly journals A Novel Approach to Maximize G-mean in Nonstationary Data with Recurrent Imbalance Shifts

2020 ◽  
Vol 18 (1) ◽  
pp. 103-113
Keyword(s):  
State Of The Art ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Streaming Data ◽  
Novel Approach ◽  
Benchmark Datasets ◽  
Boosting Algorithms ◽  
Nonstationary Data ◽  
Test Outcomes

One of the noteworthy difficulties in the classification of nonstationary data is handling data with class imbalance. Imbalanced data possess the characteristics of having a lot of samples of one class than the other. It, thusly, results in the biased accuracy of a classifier in favour of a majority class. Streaming data may have inherent imbalance resulting from the nature of dataspace or extrinsic imbalance due to its nonstationary environment. In streaming data, timely varying class priors may lead to a shift in imbalance ratio. The researchers have contemplated ensemble learning, online learning, issue of class imbalance and cost-sensitive algorithms autonomously. They have scarcely ever tended to every one of these issues mutually to deal with imbalance shift in nonstationary data. This correspondence shows a novel methodology joining these perspectives to augment G-mean in no stationary data with Recurrent Imbalance Shifts (RIS). This research modifies the state-of-the-art boosting algorithms,1) AdaC2 to get G-mean based Online AdaC2 for Recurrent Imbalance Shifts (GOA-RIS) and AGOA-RIS (Ageing and G-mean based Online AdaC2 for Recurrent Imbalance Shifts), and 2) CSB2 to get G-mean based Online CSB2 for Recurrent Imbalance Shifts (GOC-RIS) and Ageing and G-mean based Online CSB2 for Recurrent Imbalance Shifts (AGOC-RIS). The study has empirically and statistically analysed the performances of the proposed algorithms and Online AdaC2 (OA) and Online CSB2 (OC) algorithms using benchmark datasets. The test outcomes demonstrate that the proposed algorithms globally beat the performances of OA and OC

Scalabeling: Linear Slider Supported Labeling for the Classification of Streaming Data

2021 ◽  
Author(s):  
Christine Steinmeier ◽  
Jan Budke ◽  
Dominic Becking
Keyword(s):  
Streaming Data

A MULTIVARIATE TIME SERIES CLASSIFICATION METHOD FOR STREAMING DATA USING TEMPORAL METAFEATURE ABSTRACTIONS

2013 ◽  
Vol 07 (02) ◽  
pp. 173-183 ◽  
Author(s):  
TAMARA SIPES ◽  
NATASHA BALAC ◽  
HOMA KARIMABADI ◽  
NICOLE WOLTER ◽  
KENNETH NUNES ◽  
...  
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Synthetic Data ◽  
Time Lapse ◽  
Space Physics ◽  
Streaming Data ◽  
Global Features ◽  
New Approach ◽  
Important Time

In this paper we demonstrate a new approach to the classification of multivariate time series streaming data by utilizing a temporal metafeature abstractions method. The technique extracts global features and metafeatures in order to capture the necessary time-lapse information in the streams of data. The features are then used to create a static, intermediate stream representation that includes all the important time-varying information, and is suitable for analysis using the standard supervised data mining techniques. The capability of the new algorithm called MineTool-TS2 was demonstrated through its application to three datasets: UCSD Microgrid energy usage data, a space physics dataset and synthetic data.

scholarly journals Active Learning Classification of Drifted Streaming Data

2016 ◽  
Vol 80 ◽  
pp. 1724-1733 ◽  
Author(s):  
Michał Woźniak ◽  
Paweł Ksieniewicz ◽  
Bogusław Cyganek ◽  
Andrzej Kasprzak ◽  
Krzysztof Walkowiak
Keyword(s):  
Active Learning ◽  
Streaming Data

Classification of vessel activity in streaming data

2020 ◽  
Author(s):  
Ioannis Kontopoulos ◽  
Konstantinos Chatzikokolakis ◽  
Konstantinos Tserpes ◽  
Dimitris Zissis
Keyword(s):  
Streaming Data
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