The source codes of pENsemble (doi:10.21979/N9/9QM7H6)

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Part 1: Document Description
Part 2: Study Description
Part 3: Data Files Description
Part 4: Variable Description
Part 5: Other Study-Related Materials
Entire Codebook

The source codes of pENsemble

doi:10.21979/N9/9QM7H6

DR-NTU (Data)

2020-04-14

2

Pratama, Mahardhika; Pedrycz, W.; Lughofer, E., 2020, "The source codes of pENsemble", https://doi.org/10.21979/N9/9QM7H6, DR-NTU (Data), V2, UNF:6:2u0oquFqJyGbvrDzqxs+xw== [fileUNF]

The source codes of pENsemble

doi:10.21979/N9/9QM7H6

Pratama, Mahardhika (Nanyang Technological University)

Pedrycz, W. (University of Alberta, Canada)

Lughofer, E. (Johannes Kepler University Linz, Austria)

MATLAB

DR-NTU (Data)

Pratama, Mahardhika

Pratama, Mahardhika

2020-04-14

https://doi.org/10.21979/N9/9QM7H6

Computer and Information Science, Computer and Information Science, Concept drift, data streams, ensemble classifier, evolving fuzzy systems, fuzzy neural network, online learning

This link contains the MATLAB codes of pENsemble "Evolving Ensemble Fuzzy Classifier"

MATLAB codes and Dataset

10.1109/TFUZZ.2018.2796099

Pratama, M., Pedrycz, W., & Lughofer, E. (2018). Evolving ensemble fuzzy classifier. IEEE Transactions on Fuzzy Systems, 26(5), 2552-2567.

10356/105667

Pratama, M., Pedrycz, W., & Lughofer, E. (2018). Evolving ensemble fuzzy classifier. IEEE Transactions on Fuzzy Systems, 26(5), 2552-2567.

• Number of cases: 18158

• No. of variables per record: 1

• Type of File: text/tab-separated-values

UNF:6:glL3E+ZePWuvW/m8qMs2VA==

• Number of cases: 49999

• No. of variables per record: 1

• Type of File: text/tab-separated-values

UNF:6:ny4gLeZtFol5i2+Mz0e9zw==

• Number of cases: 49999

• No. of variables per record: 3

• Type of File: text/tab-separated-values

UNF:6:WFeaTXV9wfd7NkU5rR7LmA==

• Number of cases: 49999

• No. of variables per record: 1

• Type of File: text/tab-separated-values

UNF:6:yYKznv1M5Hig5abYiwWfqA==

• Number of cases: 49999

• No. of variables per record: 3

• Type of File: text/tab-separated-values

UNF:6:9VeteOWeNBhIeqDMPPq6UA==

• 0 - 0
• 2 - 2
• 0.68592 - 0.68592
• 0.1125 - 0.1125
• 9.3201 - 9.3201
• 1 - 1
• 9.4191 - 9.4191
• 1.1647 - 1.1647
• 4.4388 - 4.4388

f91025 Location:

Summary Statistics: Max. 1.0; StDev 0.46404937378315225; Mean 0.3138010794140354; Valid 18158.0; Min. 0.0;

Variable Format: numeric

Notes: UNF:6:glL3E+ZePWuvW/m8qMs2VA==

f91031 Location:

Summary Statistics: Mean 1.3582671653432996; Min. 1.0; Max. 2.0; StDev 0.47949598747044403; Valid 49999.0;

Variable Format: numeric

Notes: UNF:6:ny4gLeZtFol5i2+Mz0e9zw==

f91056 Location:

Summary Statistics: Min. 3.9599E-4; StDev 2.88965629620823; Valid 49999.0; Max. 10.0; Mean 5.012458275856517;

Variable Format: numeric

Notes: UNF:6:K/c5QphqzV04OuT3fRwHvg==

f91056 Location:

Summary Statistics: Min. 8.7176E-5; StDev 2.89859832704456; Max. 9.9998; Mean 4.9799555122433645; Valid 49999.0

Variable Format: numeric

Notes: UNF:6:pJ1/sHQrsoYOgX3tmNf+ZQ==

f91056 Location:

Summary Statistics: Valid 49999.0; Mean 4.997543278078561; StDev 2.8885885749214406; Max. 10.0; Min. 1.6348E-4;

Variable Format: numeric

Notes: UNF:6:g8uFS6NH30oYC+jw10phPw==

f91063 Location:

Summary Statistics: Min. 1.0; StDev 0.48562949783971804; Valid 49999.0; Mean 1.3809676193523848; Max. 2.0

Variable Format: numeric

Notes: UNF:6:yYKznv1M5Hig5abYiwWfqA==

f91016 Location:

Summary Statistics: Max. 9.9996; Mean 5.014194582522251; Valid 49999.0; Min. 1.0597E-4; StDev 2.888207293339758

Variable Format: numeric

Notes: UNF:6:43ISX6wj82z4dkZoCQaLYg==

f91016 Location:

Summary Statistics: Max. 10.0; Valid 49999.0; Min. 2.8626E-4; StDev 2.8889917520741544; Mean 5.005730564067082

Variable Format: numeric

Notes: UNF:6:xlLYwPAKLSTANE7esnUcCw==

f91016 Location:

Summary Statistics: Valid 49999.0; Min. 5.7718E-5; Mean 4.967997505237465; Max. 10.0; StDev 2.8874074819063273

Variable Format: numeric

Notes: UNF:6:2RNBUC9q1LV7PYiS/+ZWBA==

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10dplane.mat

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ACKNOWLEDGEMENT.txt

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car_pclass_featureweighting_ensemble.m

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carplus.mat

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carpluspclass_featureweighting_ensemble.m

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dataset.txt

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dplane_pclass_featureweighting_ensemble.m

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dplan_pclass_featureweighting_ensemble.m

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electricity_pclass_featureweighting_ensemble.m

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electricitypricing_pclass_featureweighting_ensemble.m

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findcommon.m

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gaussiam.mat

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gaussian_pclass_featureweighting_ensemble.m

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gaussian_pclass_featureweighting_ensemble.m

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hyperplane.mat

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hyperplane_pclass_featureweighting_ensemble.m

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hyperplane_pclass_featureweighting_ensemble.m

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irisplus.mat

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irisplus_pclass_featureweighting_ensemble.m

text/x-matlab

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irisplus_pclass_featureweighting_ensemble.m

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LICENSE.md

text/markdown

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line.mat

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line_pclass_featureweighting_ensemble.m

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line_pclass_featureweighting_ensemble.m

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modify_dataset_zero_class.m

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modify_dataset_zero_class.m

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NEweather_data.csv

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normal_class.m

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normal_class.m

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pclass_axis_parallel.p

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pclass_axis_parallel_updated.p

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pclass_local_mod_improved_feature_weighting_multivariate4.p

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pclass_local_mod_improved_feature_weighting_multivariate4.p

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pclass_multivariate_update1.p

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pclass_multivariate_update1.p

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processed_dataset.xls

application/vnd.ms-excel

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README.md

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run_pensemble_prequential.m

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sea.mat

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sea_pclass_featureweighting_ensemble.m

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sea_pclass_featureweighting_ensemble.m

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SEA_readme.txt

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sinH.mat

application/matlab-mat

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sinH_pclass_featureweighting_ensemble.m

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sinH_pclass_featureweighting_ensemble.m

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sin.mat

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sin_pclass_featureweighting_ensemble.m

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sin_pclass_featureweighting_ensemble.m

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susy_pclass_featureweighting_ensemble.m

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susypclass_featureweighting_ensemble.m

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title

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title

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title

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title

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weather_pclass_featureweighting_ensemble.m

text/x-matlab

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weather_pclass_featureweighting_ensemble.m

text/x-matlab