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.

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 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 Resolving longitudinal amplitude and phase information of two continuous data streams for high-speed and real-time processing

2009 ◽  
Vol 7 ◽  
pp. 133-137 ◽  
Author(s):  
A. Guntoro ◽  
M. Glesner
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
High Speed ◽  
Hardware Implementation ◽  
Software Implementation ◽  
Phase Detector ◽  
Continuous Data ◽  
Process Data ◽  
Real Time Processing ◽  
High Speed Data

Abstract. Although there is an increase of performance in DSPs, due to its nature of execution a DSP could not perform high-speed data processing on a continuous data stream. In this paper we discuss the hardware implementation of the amplitude and phase detector and the validation block on a FPGA. Contrary to the software implementation which can only process data stream as high as 1.5 MHz, the hardware approach is 225 times faster and introduces much less latency.

scholarly journals Handling Concept Drift in Data Stream Classification

2019 ◽  
Vol 8 (10) ◽  
pp. 548-550
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Concept Drift ◽  
Research Work ◽  
Class Imbalance ◽  
Class Imbalance Problem ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Imbalance Problem ◽  
Major Factors

Data Streams are having huge volume and it can-not be stored permanently in the memory for processing. In this paper we would be mainly focusing on issues in data stream, the major factors which are affecting the accuracy of classifier like imbalance class and Concept Drift. The drift in Data Stream mining refers to the change in data. Such as Class imbalance problem notifies that the samples are in the classes are not equal. In our research work we are trying to identify the change (Drift) in data, we are trying to detect Imbalance class and noise from changed data. And According to the type of drift we are applying the algorithms and trying to make the stream more balance and noise free to improve classifier’s accuracy.

Deterministic Concept Drift Detection in Ensemble Classifier Based Data Stream Classification Process

2019 ◽  
Vol 11 (1) ◽  
pp. 29-48 ◽  
Author(s):  
Mohammed Ahmed Ali Abdualrhman ◽  
M C Padma
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Concept Drift ◽  
Ensemble Classifier ◽  
Experimental Result ◽  
Process Time ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Proposed Model ◽  
Concept Drift Detection

The data in streaming environment tends to be non-stationary. Hence, frequent and irregular changes occur in data, which usually denotes as a concept drift related to the process of classifying data streams. Depiction of the concept drift in traditional phase of data stream mining demands availability of labelled samples; however, incorporating the label to a streamlining transaction is infeasible in terms of process time and resource utilization. In this article, deterministic concept drift detection (DCDD) in ensemble classifier-based data stream classification process is proposed, which can depict a concept drift regardless of the labels assigned to samples. The depicted model of DCDD is evaluated by experimental study on dataset called poker-hand. The experimental result showing that the proposed model is accurate and scalable to detect concept drift with high drift detection rate and minimal false alarming and missing rate that compared to other contemporary models.

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.

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

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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