scholarly journals Handling concept drifts and limited label problems using semi-supervised combine-merge Gaussian mixture model

2021 ◽  
Vol 10 (6) ◽  
pp. 3361-3368
Author(s):  
Ibnu Daqiqil Id ◽  
Pardomuan Robinson Sihombing ◽  
Supratman Zakir
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
High Speed ◽  
Concept Drift ◽  
Model Performance ◽  
Gaussian Mixture ◽  
Model Adaptation ◽  
Model Accuracy ◽  
Concept Drifts ◽  
Do So

When predicting data streams, changes in data distribution may decrease model accuracy over time, thereby making the model obsolete. This phenomenon is known as concept drift. Detecting concept drifts and then adapting to them are critical operations to maintain model performance. However, model adaptation can only be made if labeled data is available. Labeling data is both costly and time-consuming because it has to be done by humans. Only part of the data can be labeled in the data stream because the data size is massive and appears at high speed. To solve these problems simultaneously, we apply a technique to update the model by employing both labeled and unlabeled instances to do so. The experiment results show that our proposed method can adapt to the concept drift with pseudo-labels and maintain its accuracy even though label availability is drastically reduced from 95% to 5%. The proposed method also has the highest overall accuracy and outperforms other methods in 5 of 10 datasets.

scholarly journals Data Stream Classification Based on the Gamma Classifier

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Abril Valeria Uriarte-Arcia ◽  
Itzamá López-Yáñez ◽  
Cornelio Yáñez-Márquez ◽  
João Gama ◽  
Oscar Camacho-Nieto
Keyword(s):  
Data Streams ◽  
Time Management ◽  
Data Stream ◽  
High Speed ◽  
Concept Drift ◽  
Synthetic Data ◽  
Continuous Data ◽  
Data Generation ◽  
Underlying Distribution ◽  
Data Stream Classification

The ever increasing data generation confronts us with the problem of handling online massive amounts of information. One of the biggest challenges is how to extract valuable information from these massive continuous data streams during single scanning. In a data stream context, data arrive continuously at high speed; therefore the algorithms developed to address this context must be efficient regarding memory and time management and capable of detecting changes over time in the underlying distribution that generated the data. This work describes a novel method for the task of pattern classification over a continuous data stream based on an associative model. The proposed method is based on the Gamma classifier, which is inspired by the Alpha-Beta associative memories, which are both supervised pattern recognition models. The proposed method is capable of handling the space and time constrain inherent to data stream scenarios. The Data Streaming Gamma classifier (DS-Gamma classifier) implements a sliding window approach to provide concept drift detection and a forgetting mechanism. In order to test the classifier, several experiments were performed using different data stream scenarios with real and synthetic data streams. The experimental results show that the method exhibits competitive performance when compared to other state-of-the-art algorithms.

Dynamically Adjusting Diversity in Ensembles for the Classification of Data Streams with Concept Drift

2021 ◽  
Vol 16 (2) ◽  
pp. 1-30
Author(s):  
Juan I. G. Hidalgo ◽  
Silas G. T. C. Santos ◽  
Roberto S. M. Barros
Keyword(s):  
Parameter Estimation ◽  
Real World ◽  
Data Streams ◽  
Data Stream ◽  
Concept Drift ◽  
Estimation Method ◽  
Estimation Procedure ◽  
Dynamic Parameter ◽  
Real World Datasets ◽  
Concept Drifts

A data stream can be defined as a system that continually generates a lot of data over time. Today, processing data streams requires new demands and challenging tasks in the data mining and machine learning areas. Concept Drift is a problem commonly characterized as changes in the distribution of the data within a data stream. The implementation of new methods for dealing with data streams where concept drifts occur requires algorithms that can adapt to several scenarios to improve its performance in the different experimental situations where they are tested. This research proposes a strategy for dynamic parameter adjustment in the presence of concept drifts. Parameter Estimation Procedure (PEP) is a general method proposed for dynamically adjusting parameters which is applied to the diversity parameter (λ) of several classification ensembles commonly used in the area. To this end, the proposed estimation method (PEP) was used to create Boosting-like Online Learning Ensemble with Parameter Estimation (BOLE-PE), Online AdaBoost-based M1 with Parameter Estimation (OABM1-PE), and Oza and Russell’s Online Bagging with Parameter Estimation (OzaBag-PE), based on the existing ensembles BOLE, OABM1, and OzaBag, respectively. To validate them, experiments were performed with artificial and real-world datasets using Hoeffding Tree (HT) as base classifier. The accuracy results were statistically evaluated using a variation of the Friedman test and the Nemenyi post-hoc test. The experimental results showed that the application of the dynamic estimation in the diversity parameter (λ) produced good results in most scenarios, i.e., the modified methods have improved accuracy in the experiments with both artificial and real-world datasets.

Knowledge Discovery From Evolving Data Streams

2019 ◽  
pp. 19-39
Author(s):  
Prasanna Lakshmi Kompalli
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Data Stream ◽  
Concept Drift ◽  
Data Stream Mining ◽  
Time Data ◽  
Stream Mining ◽  
New Challenges ◽  
Mining Data Streams ◽  
Different Sources

Data coming from different sources is referred to as data streams. Data stream mining is an online learning technique where each data point must be processed as the data arrives and discarded as the processing is completed. Progress of technologies has resulted in the monitoring these data streams in real time. Data streams has created many new challenges to the researchers in real time. The main features of this type of data are they are fast flowing, large amounts of data which are continuous and growing in nature, and characteristics of data might change in course of time which is termed as concept drift. This chapter addresses the problems in mining data streams with concept drift. Due to which, isolating the correct literature would be a grueling task for researchers and practitioners. This chapter tries to provide a solution as it would be an amalgamation of all techniques used for data stream mining with concept drift.

scholarly journals A Survey of Challenges Facing Streaming Data

2020 ◽  
Vol 8 (4) ◽  
pp. 63-73
Author(s):  
Sikha Bagui ◽  
Katie Jin
Keyword(s):  
Data Reduction ◽  
Data Streams ◽  
Data Stream ◽  
Stream Processing ◽  
Streaming Data ◽  
Data Detection ◽  
Data Stream Processing ◽  
The Face ◽  
Concept Drifts

This survey performs a thorough enumeration and analysis of existing methods for data stream processing. It is a survey of the challenges facing streaming data. The challenges addressed are preprocessing of streaming data, detection and dealing with concept drifts in streaming data, data reduction in the face of data streams, approximate queries and blocking operations in streaming data.

scholarly journals Cost-Sensitive Classification for Evolving Data Streams with Concept Drift and Class Imbalance

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yange Sun ◽  
Meng Li ◽  
Lei Li ◽  
Han Shao ◽  
Yi Sun
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Learning Strategy ◽  
Concept Drift ◽  
Class Imbalance ◽  
Data Preprocessing ◽  
Cost Information ◽  
Detection Mechanism ◽  
Stream Classification ◽  
Data Stream Classification

Class imbalance and concept drift are two primary principles that exist concurrently in data stream classification. Although the two issues have drawn enough attention separately, the joint treatment largely remains unexplored. Moreover, the class imbalance issue is further complicated if data streams with concept drift. A novel Cost-Sensitive based Data Stream (CSDS) classification is introduced to overcome the two issues simultaneously. The CSDS considers cost information during the procedures of data preprocessing and classification. During the data preprocessing, a cost-sensitive learning strategy is introduced into the ReliefF algorithm for alleviating the class imbalance at the data level. In the classification process, a cost-sensitive weighting schema is devised to enhance the overall performance of the ensemble. Besides, a change detection mechanism is embedded in our algorithm, which guarantees that an ensemble can capture and react to drift promptly. Experimental results validate that our method can obtain better classification results under different imbalanced concept drifting data stream scenarios.

scholarly journals Improvement of Data Stream Decision Trees

2022 ◽  
Vol 18 (1) ◽  
pp. 1-17
Author(s):  
Sarah Nait Bahloul ◽  
Oussama Abderrahim ◽  
Aya Ichrak Benhadj Amar ◽  
Mohammed Yacine Bouhedadja
Keyword(s):  
Decision Trees ◽  
Data Streams ◽  
Data Stream ◽  
High Speed ◽  
Computational Cost ◽  
Research Area ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Hoeffding Tree ◽  
Benchmark Datasets

The classification of data streams has become a significant and active research area. The principal characteristics of data streams are a large amount of arrival data, the high speed and rate of its arrival, and the change of their nature and distribution over time. Hoeffding Tree is a method to, incrementally, build decision trees. Since its proposition in the literature, it has become one of the most popular tools of data stream classification. Several improvements have since emerged. Hoeffding Anytime Tree was recently introduced and is considered one of the most promising algorithms. It offers a higher accuracy compared to the Hoeffding Tree in most scenarios, at a small additional computational cost. In this work, the authors contribute by proposing three improvements to the Hoeffding Anytime Tree. The improvements are tested on known benchmark datasets. The experimental results show that two of the proposed variants make better usage of Hoeffding Anytime Tree’s properties. They learn faster while providing the same desired accuracy.

scholarly journals Microcluster-Based Incremental Ensemble Learning for Noisy, Nonstationary Data Streams

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Sanmin Liu ◽  
Shan Xue ◽  
Fanzhen Liu ◽  
Jieren Cheng ◽  
Xiulai Li ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Data Streams ◽  
Data Stream ◽  
Concept Drift ◽  
Majority Vote ◽  
Stream Classification ◽  
Model Stability ◽  
Data Stream Classification ◽  
Nonstationary Data ◽  
Synthetic Datasets

Data stream classification becomes a promising prediction work with relevance to many practical environments. However, under the environment of concept drift and noise, the research of data stream classification faces lots of challenges. Hence, a new incremental ensemble model is presented for classifying nonstationary data streams with noise. Our approach integrates three strategies: incremental learning to monitor and adapt to concept drift; ensemble learning to improve model stability; and a microclustering procedure that distinguishes drift from noise and predicts the labels of incoming instances via majority vote. Experiments with two synthetic datasets designed to test for both gradual and abrupt drift show that our method provides more accurate classification in nonstationary data streams with noise than the two popular baselines.

scholarly journals Concept Drift Detection in Data Stream Clustering and its Application on Weather Data

2020 ◽  
Vol 11 (1) ◽  
pp. 67-85 ◽  
Author(s):  
Namitha K. ◽  
Santhosh Kumar G.
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Weather Forecasting ◽  
Concept Drift ◽  
Clustering Algorithms ◽  
Weather Data ◽  
Stream Clustering ◽  
Cluster Evolution ◽  
Data Stream Clustering ◽  
Concept Drift Detection

This article presents a stream mining framework to cluster the data stream and monitor its evolution. Even though concept drift is expected to be present in data streams, explicit drift detection is rarely done in stream clustering algorithms. The proposed framework is capable of explicit concept drift detection and cluster evolution analysis. Concept drift is caused by the changes in data distribution over time. Relationship between concept drift and the occurrence of physical events has been studied by applying the framework on the weather data stream. Experiments led to the conclusion that the concept drift accompanied by a change in the number of clusters indicates a significant weather event. This kind of online monitoring and its results can be utilized in weather forecasting systems in various ways. Weather data streams produced by automatic weather stations (AWS) are used to conduct this study.

scholarly journals Learning in the presence of concept recurrence in data stream clustering

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
K. Namitha ◽  
G. Santhosh Kumar
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Concept Drift ◽  
Synthetic Data ◽  
Real World Data ◽  
Stream Classification ◽  
Stream Clustering ◽  
The Past ◽  
Data Stream Clustering ◽  
Learning Scenarios

Abstract In the case of real-world data streams, the underlying data distribution will not be static; it is subject to variation over time, which is known as the primary reason for concept drift. Concept drift poses severe problems to the accuracy of a model in online learning scenarios. The recurring concept is a particular case of concept drift where the concepts already seen in the past reappear as the stream evolves. This problem is not yet studied in the context of stream clustering. This paper proposes a novel algorithm for identifying the recurring concepts in data stream clustering. During concept recurrence, the most matching model is retrieved from the repository and reused. The algorithm has minimum memory requirements and works online with the stream. Some of the concepts and definitions, already familiar in concept recurrence studies of stream classification have been redefined for clustering. The experiments conducted on real and synthetic data streams reveal that the proposed algorithm has the potential to identify recurring concepts.

scholarly journals Minority Resampling Based Ensemble Framework Using Enhanced Early Drift Detection Method For Imbalanced Data Streams

2021 ◽  
Author(s):  
Priya S ◽  
Annie Uthra
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Detection Method ◽  
Concept Drift ◽  
Class Imbalance ◽  
Current Data ◽  
Classification Model ◽  
Ensemble Classifiers ◽  
K Nearest Neighbor ◽  
Jaccard Similarity

Abstract As the data mining applications are increasing popularly, large volumes of data streams are generated over the period of time. The main problem in data streams is that it exhibits a high degree of class imbalance and distribution of data changes over time. In this paper, Timely Drift Detection and Minority Resampling Technique (TDDMRT) based on K-nearest neighbor and Jaccard similarity is proposed to handle the class imbalance by finding the current ratio of class labels. The Enhanced Early Drift Detection Method (EEDDM) is proposed for detecting the concept drift and the Minority Resampling Method (KNN-JS) determines whether the current data stream should be regarded as imbalance and it resamples the minority instances in the drifting data stream. The K-Nearest Neighbors technique is used to resample the minority classes and the Jaccard similarity measure is established over the resampled data to generate the synthetic data similar to the original data and it is handled by ensemble classifiers. The proposed ensemble based classification model outperforms the existing over sampling and under sampling techniques with accuracy of 98.52%.

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