A clustering and ensemble based classifier for data stream classification

Wankhade, Kapil; Jondhale, Kalpana C.; Dongre, Snehlata

doi:10.1016/j.asoc.2020.107076

Cited by 36 publications

(4 citation statements)

References 47 publications

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“…Based on web crawling technology, database data is extracted. By crawling, the data is dispersed in the direction of migration node width in order, and the corresponding database data is extracted according to standards [7]. By considering the straight line with a slope approaching 0 and selecting the line that meets the standard, assuming the optimal slope of k , the key feature values for database data capture are achieved through domestic database crawlers, represented as:…”

Section: Data Extraction For Domestic Database Migrationmentioning

confidence: 99%

A method for identifying similar duplicate records in domestic database migration based on an improved LSSVM algorithm

Ding,

Zhang,

et al. 2024

International Conference on Signal Processing and Communication Security (ICSPCS 2024)

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Similar duplicate record recognition can help us effectively remove duplicate and redundant data, improve data quality and consistency. To this end, a domestic database migration similar duplicate record recognition method based on an improved LSSVM algorithm is proposed. Extract domestic database migration data based on crawler structure; Reduce noise and encode the extracted migration data separately to improve data quality; Based on the time cycle, using mutual information as a correlation measure, extract and fuse duplicate features of transfer data; Construct a similar duplicate record recognition model using the LSSVM algorithm, and improve the LSSVM algorithm using the quantum particle swarm optimization algorithm to achieve similar duplicate record recognition in domestic database migration. The experimental results show that the method has high recognition ROC and F1 values, which improves recognition accuracy and efficiency.

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Section: Data Extraction For Domestic Database Migrationmentioning

confidence: 99%

A method for identifying similar duplicate records in domestic database migration based on an improved LSSVM algorithm

Ding,

Zhang,

et al. 2024

International Conference on Signal Processing and Communication Security (ICSPCS 2024)

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“…Ensemble Learning is a machine learning technique that combines many classifiers to create a final prediction. It is suitable for understanding complex data patterns in decision making and prediction (Wankhade et al, 2021). In software defect prediction, there are two main problems: regression (predicting the number of defects) and classification (predicting instance labels) and it has two phases of training base classifiers and building the final prediction model with the results of the base classifiers (Logesh et al, 2020).…”

Section: Theorymentioning

confidence: 99%

Software Defect Prediction For Quality Evaluation Using Learning Techniques Ensemble Stacking

Kusuma,

Windu Gata,

Sigit Kurniawan

et al. 2023

j. inspir.

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This research aims to improve the software quality and effectiveness of zakat management by the National Amil Zakat Agency (BAZNAS) through the development of a software defect prediction model (SDPM). We used machine learning techniques and ensemble stacking approach on the "Masjid Tower" dataset containing 228 records and 34 attributes. The preprocessing process involved label encoding, feature selection with Pearson correlation, standard normalization, and the use of SMOTE to handle data imbalance. We performed hyperparameter tuning with grid search CV on Machine Learning algorithms such as Ada Boost and Gradient Boosting. The results showed that the ensemble stacking approach with a combination of Gradient Boosting, Ada Boost, Decision Tree, Bayesian Ridge, and LightGBM meta learner algorithms provided high accuracy with R2 score reaching 0.97, MAE of 0.037, and MSE of 0.006. This finding proves that the ensemble stacking approach is able to overcome the problem of software defects with accurate prediction results, provide useful guidance in the management of zakat and other software applications, and has the potential to improve software quality and the effectiveness of BAZNAS in managing zakat.

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“…Specifically, data stream processing must be done in a single pass and cannot mine repeatedly according to Morales and Samoa [5]. Apart from these methods some of the investigators [6][7][8] paid attention and publicized on unsupervised learning for data stream using cluster methods. Because this paper also depends on clustering, it is worth mentioning some of the existing works with respect to this topic.…”

Section: Related Workmentioning

confidence: 99%

The Ensemble of Unsupervised Incremental Learning Algorithm for Time Series Data

Beulah

Krishnaraja²

2022

IJE

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A clustering and ensemble based classifier for data stream classification

Cited by 36 publications

References 47 publications

A method for identifying similar duplicate records in domestic database migration based on an improved LSSVM algorithm

A method for identifying similar duplicate records in domestic database migration based on an improved LSSVM algorithm

Software Defect Prediction For Quality Evaluation Using Learning Techniques Ensemble Stacking

The Ensemble of Unsupervised Incremental Learning Algorithm for Time Series Data

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