A dynamic hierarchical incremental learning-based supervised clustering for data stream with considering concept drift

2022 ◽  
Author(s):  
Soheila Nikpour ◽  
Shahrokh Asadi
Keyword(s):  
Incremental Learning ◽  
Data Stream ◽  
Concept Drift ◽  
Supervised Clustering

Incremental Learning on Non-stationary Data Stream using Ensemble Approach

2016 ◽  
Vol 6 (4) ◽  
pp. 1811 ◽  
Author(s):  
Meenakshi Anurag Thalor ◽  
Shrishailapa Patil
Keyword(s):  
Machine Learning ◽  
Incremental Learning ◽  
Data Stream ◽  
Recommendation System ◽  
Concept Drift ◽  
Joint Probability ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Joint Probability Distribution ◽  
Changes Over Time

<span lang="EN-US">Incremental Learning on non stationary distribution has been shown to be a very challenging problem in machine learning and data mining, because the joint probability distribution between the data and classes changes over time. Many real time problems suffer concept drift as they changes with time. For example, an advertisement recommendation system, in which customer’s behavior may change depending on the season of the year, on the inflation and on new products made available. An extra challenge arises when the classes to be learned are not represented equally in the training data i.e. classes are imbalanced, as most machine learning algorithms work well only when the training data  is balanced. The objective of this paper is to develop an ensemble based classification algorithm for non-stationary data stream (ENSDS) with focus on two-class problems. In addition, we are presenting here an exhaustive comparison of purposed algorithms with state-of-the-art classification approaches using different evaluation measures like recall, f-measure and g-mean</span>

scholarly journals Incremental Learning on Non-stationary Data Stream using Ensemble Approach

2016 ◽  
Vol 6 (4) ◽  
pp. 1811
Author(s):  
Meenakshi Anurag Thalor ◽  
Shrishailapa Patil
Keyword(s):  
Machine Learning ◽  
Incremental Learning ◽  
Data Stream ◽  
Recommendation System ◽  
Concept Drift ◽  
Joint Probability ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Joint Probability Distribution ◽  
Changes Over Time

<span lang="EN-US">Incremental Learning on non stationary distribution has been shown to be a very challenging problem in machine learning and data mining, because the joint probability distribution between the data and classes changes over time. Many real time problems suffer concept drift as they changes with time. For example, an advertisement recommendation system, in which customer’s behavior may change depending on the season of the year, on the inflation and on new products made available. An extra challenge arises when the classes to be learned are not represented equally in the training data i.e. classes are imbalanced, as most machine learning algorithms work well only when the training data  is balanced. The objective of this paper is to develop an ensemble based classification algorithm for non-stationary data stream (ENSDS) with focus on two-class problems. In addition, we are presenting here an exhaustive comparison of purposed algorithms with state-of-the-art classification approaches using different evaluation measures like recall, f-measure and g-mean</span>

Analyzing and repairing concept drift adaptation in data stream classification

2021 ◽  
Author(s):  
Ben Halstead ◽  
Yun Sing Koh ◽  
Patricia Riddle ◽  
Russel Pears ◽  
Mykola Pechenizkiy ◽  
...  
Keyword(s):  
Data Stream ◽  
Concept Drift ◽  
Stream Classification ◽  
Data Stream Classification

scholarly journals Incremental Learning Framework for Mining Big Data Stream

2022 ◽  
Vol 71 (2) ◽  
pp. 2901-2921
Author(s):  
Alaa Eisa ◽  
Nora EL-Rashidy ◽  
Mohammad Dahman Alshehri ◽  
Hazem M. El-bakry ◽  
Samir Abdelrazek
Keyword(s):  
Big Data ◽  
Incremental Learning ◽  
Data Stream ◽  
Learning Framework

scholarly journals Learning from Ontology Streams with Semantic Concept Drift

2017 ◽  
Author(s):  
Jiaoyan Chen ◽  
Freddy Lecue ◽  
Jeff Z. Pan ◽  
Huajun Chen
Keyword(s):  
Semantic Web ◽  
Data Stream ◽  
Concept Drift ◽  
Data Distribution ◽  
Accurate Prediction ◽  
Knowledge Structures ◽  
Semantic Concept ◽  
Web Data ◽  
Semantic Inference

Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic embeddings. The experiments show accurate prediction with data from Dublin and Beijing.

scholarly journals An Improved Differential Evolution Algorithm for Data Stream Clustering

2019 ◽  
Vol 9 (4) ◽  
pp. 2659
Author(s):  
Bhaskar Adepu ◽  
Jayadev Gyani ◽  
G. Narsimha
Keyword(s):  
Differential Evolution ◽  
Data Stream ◽  
Concept Drift ◽  
Differential Evolution Algorithm ◽  
Optimization Approach ◽  
Stream Clustering ◽  
Data Stream Clustering ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm ◽  
Measure Estimate

A Few algorithms were actualized by the analysts for performing clustering of data streams. Most of these algorithms require that the number of clusters (K) has to be fixed by the customer based on input data and it can be kept settled all through the clustering process. Stream clustering has faced few difficulties in picking up K. In this paper, we propose an efficient approach for data stream clustering by embracing an Improved Differential Evolution (IDE) algorithm. The IDE algorithm is one of the quick, powerful and productive global optimization approach for programmed clustering. In our proposed approach, we additionally apply an entropy based method for distinguishing the concept drift in the data stream and in this way updating the clustering procedure online. We demonstrated that our proposed method is contrasted with Genetic Algorithm and identified as proficient optimization algorithm. The performance of our proposed technique is assessed and cr eates the accuracy of 92.29%, the precision is 86.96%, recall is 90.30% and F-measure estimate is 88.60%.

Incremental Learning of Concept Drift from Streaming Imbalanced Data

2013 ◽  
Vol 25 (10) ◽  
pp. 2283-2301 ◽  
Author(s):  
Gregory Ditzler ◽  
Robi Polikar
Keyword(s):  
Incremental Learning ◽  
Concept Drift ◽  
Imbalanced Data

Heuristic ensemble for unsupervised detection of multiple types of concept drift in data stream classification

2021 ◽  
pp. 1-14
Author(s):  
Hanqing Hu ◽  
Mehmed Kantardzic
Keyword(s):  
Data Stream ◽  
Concept Drift ◽  
False Alarms ◽  
Detection Accuracy ◽  
Real World Data ◽  
Traditional Concept ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Detection Algorithms ◽  
Concept Drift Detection

Real-world data stream classification often deals with multiple types of concept drift, categorized by change characteristics such as speed, distribution, and severity. When labels are unavailable, traditional concept drift detection algorithms, used in stream classification frameworks, are often focused on only one type of concept drift. To overcome the limitations of traditional detection algorithms, this study proposed a Heuristic Ensemble Framework for Drift Detection (HEFDD). HEFDD aims to detect all types of concept drift by employing an ensemble of selected concept drift detection algorithms, each capable of detecting at least one type of concept drift. Experimental results show HEFDD provides significant improvement based on the z-score test when comparing detection accuracy with state-of-the-art individual algorithms. At the same time, HEFDD is able to reduce false alarms generated by individual concept drift detection algorithms.

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