Stream Classification

2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jerzy Stefanowski ◽  
Dariusz Brzezinski
Keyword(s):  
Stream Classification

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

Chi-MFlexDT: Chi-square-based multi flexible fuzzy decision tree for data stream classification

2021 ◽  
pp. 107301
Author(s):  
Farnaz Mahan ◽  
Maryam Mohammadzad ◽  
Seyyed Meysam Rozekhani ◽  
Witold Pedrycz
Keyword(s):  
Decision Tree ◽  
Data Stream ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Chi Square ◽  
Stream Classification ◽  
Data Stream Classification

VMV-GCN: Volumetric Multi-View Based Graph CNN for Event Stream Classification

2022 ◽  
pp. 1-1
Author(s):  
Bochen Xie ◽  
Yongjian Deng ◽  
Zhanpeng Shao ◽  
Hai Liu ◽  
You-Fu Li
Keyword(s):  
Stream Classification ◽  
Event Stream

scholarly journals Comparing the performance of different stream classification systems using aquatic macroinvertebrates

2013 ◽  
Vol 25 (4) ◽  
pp. 406-417 ◽  
Author(s):  
Márlon de Castro Vasconcelos ◽  
Adriano Sanches Melo ◽  
Albano Schwarzbold
Keyword(s):  
Randomization Test ◽  
Classification Systems ◽  
Data Type ◽  
Taxonomic Resolution ◽  
Stream Classification ◽  
Environment Variables ◽  
Family Level ◽  
Community Data ◽  
Performance Results ◽  
Classification Strength

AIM: We evaluated five stream classification systems observing: 1) differences in richness, abundance and macroinvertebrates communities among stream classes within classification systems; and 2) whether classification systems present better performance using macroinvertebrates. Additionally, we evaluated the effects of taxonomic resolution and data type (abundance and presence) on results. METHODS: Five stream classification systems were used, two based on hydroregions, one based on ecoregions by FEOW, a fourth one based on stream orders and the last one based on clusters of environment variables sampled in 37 streams at Rio Grande do Sul state, Brazil. We used a randomization test to evaluate differences of richness and abundance, a db-MANOVA to evaluate the differences of species assemblages and Classification Strength (CS) to evaluate the classifications performance. RESULTS: There were differences of richness and abundance among stream classes within each stream classification. The same result was found for community data, except for stream order classifications in family level. We observed that stream classes obtained for each stream classification differed in terms of environment variables (db-MANOVA). The classification based on environment variables showed higher CS values than other classification systems. The taxonomic resolution was important to the observed results. Data on genera level presented CS values 12% higher than family level for cluster classification, and the data type was dependent on the classification system and taxonomic resolution employed. CONCLUSION: Our results indicate that classifications based on cluster of environment variables was better than other stream classification systems, and similar results using genera level can be obtained for management programs using family resolution in a geographical context similar to this study.

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