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Proportional association based roi model
1. | School of Mathematics and Information Sciences, Guangzhou University, Guangzhou, 510006, China |
2. | Clearpier Inc., 1300-121 Richmond St. W., Toronto, Ontario M5H 2K1 Canada |
3. | School of Mathematics and Information Sciences, Guangzhou University, Guangzhou, 510006, China |
Based on a local-to-global proportional association measure proposed by Huang, Shi and Wang [
References:
[1] |
C. Cornforth,
What makes boards effctive? an examination of the relationships between board inputs, structures, processes and effctiveness in non-profit organisations, Corporate Governance: An International Review, 9 (2011), 217-227.
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[2] |
L. L. Fong, M. S. Squillante and R. E. Hough,
Computer resource proportional utilization and response time scheduling, US Patent, 6 (2001), 263-359.
|
[3] |
L. A. Goodman,
A single general method for the analysis of cross-classifed data: Reconciliation, and synthesis of some methods of pearson, yule, and fisher, and also some methods of correspondence analysis and association analysis, Journal of the American Statistical Association, 91 (1996), 408-428.
doi: 10.1080/01621459.1996.10476702. |
[4] |
L. A. Goodman and W. H. Kruskal, Measures of Association for Cross Classifications Springer, 1979. |
[5] |
M. F. Gregor, L. Yang, E. Fabbrini, B. S. Mohammed, J. C. Eagon, G. S. Hotamisligil and S. Klein,
Endoplasmic reticulum stress is reduced in tissues of obese subjects after weight loss, Diabetes, 58 (2009), 693-700.
doi: 10.2337/db08-1220. |
[6] |
W. Huang and Y. Pan,
On balancing between optimal and proportional categorical predictions, Big Data and Information Analytics, 1 (2016), 129-137.
doi: 10.3934/bdia.2016.1.129. |
[7] |
W. Huang, Y. Pan and J. Wu,
Supervised discretization with GK-τ, Procedia Computer Science, 17 (2013), 114-120.
|
[8] |
W. Huang, Y. Pan and J. Wu,
Performance measures of rare events targeting, International Journal of Data Analysis Techniques and Strategies, 6 (2014), 105-120.
doi: 10.1504/IJDATS.2014.062450. |
[9] |
W. Huang, Y. Shi and X. Wang,
A nominal association matrix with feature selection for categorical data, Comunications in Statistic -Theory and Methods, 46 (2017), 7798-7819.
doi: 10.1080/03610926.2014.930911. |
[10] |
H. Hwang, T. Jung and E. Suh,
An ltv model and customer segmentation based on customer value: A case study on the wireless telecommunication industry, Expert Systems with Applications, 26 (2004), 181-188.
doi: 10.1016/S0957-4174(03)00133-7. |
[11] |
T. Lin, Y. Yang and H. T. Shiau,
A work weighted state vector control method for geometrically nonlinear analysis, Computers and Structures, 46 (1993), 689-694.
doi: 10.1016/0045-7949(93)90397-V. |
[12] |
C. X. Ling and C. Li,
Data mining for direct marketing: Problems and solutions, in Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), AAAI Press, (1998), 73-79.
|
[13] |
J. R. Quinlan,
Induction of decision trees, Machine Learning, 1 (1986), 81-106.
doi: 10.1007/BF00116251. |
show all references
References:
[1] |
C. Cornforth,
What makes boards effctive? an examination of the relationships between board inputs, structures, processes and effctiveness in non-profit organisations, Corporate Governance: An International Review, 9 (2011), 217-227.
|
[2] |
L. L. Fong, M. S. Squillante and R. E. Hough,
Computer resource proportional utilization and response time scheduling, US Patent, 6 (2001), 263-359.
|
[3] |
L. A. Goodman,
A single general method for the analysis of cross-classifed data: Reconciliation, and synthesis of some methods of pearson, yule, and fisher, and also some methods of correspondence analysis and association analysis, Journal of the American Statistical Association, 91 (1996), 408-428.
doi: 10.1080/01621459.1996.10476702. |
[4] |
L. A. Goodman and W. H. Kruskal, Measures of Association for Cross Classifications Springer, 1979. |
[5] |
M. F. Gregor, L. Yang, E. Fabbrini, B. S. Mohammed, J. C. Eagon, G. S. Hotamisligil and S. Klein,
Endoplasmic reticulum stress is reduced in tissues of obese subjects after weight loss, Diabetes, 58 (2009), 693-700.
doi: 10.2337/db08-1220. |
[6] |
W. Huang and Y. Pan,
On balancing between optimal and proportional categorical predictions, Big Data and Information Analytics, 1 (2016), 129-137.
doi: 10.3934/bdia.2016.1.129. |
[7] |
W. Huang, Y. Pan and J. Wu,
Supervised discretization with GK-τ, Procedia Computer Science, 17 (2013), 114-120.
|
[8] |
W. Huang, Y. Pan and J. Wu,
Performance measures of rare events targeting, International Journal of Data Analysis Techniques and Strategies, 6 (2014), 105-120.
doi: 10.1504/IJDATS.2014.062450. |
[9] |
W. Huang, Y. Shi and X. Wang,
A nominal association matrix with feature selection for categorical data, Comunications in Statistic -Theory and Methods, 46 (2017), 7798-7819.
doi: 10.1080/03610926.2014.930911. |
[10] |
H. Hwang, T. Jung and E. Suh,
An ltv model and customer segmentation based on customer value: A case study on the wireless telecommunication industry, Expert Systems with Applications, 26 (2004), 181-188.
doi: 10.1016/S0957-4174(03)00133-7. |
[11] |
T. Lin, Y. Yang and H. T. Shiau,
A work weighted state vector control method for geometrically nonlinear analysis, Computers and Structures, 46 (1993), 689-694.
doi: 10.1016/0045-7949(93)90397-V. |
[12] |
C. X. Ling and C. Li,
Data mining for direct marketing: Problems and solutions, in Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), AAAI Press, (1998), 73-79.
|
[13] |
J. R. Quinlan,
Induction of decision trees, Machine Learning, 1 (1986), 81-106.
doi: 10.1007/BF00116251. |
1000 | 100 | 500 | 400 | 500 | 300 | 200 | 1500 | |||
200 | 1500 | 500 | 300 | 500 | 400 | 400 | 50 | |||
400 | 50 | 500 | 500 | 500 | 500 | 300 | 700 | |||
300 | 700 | 500 | 400 | 500 | 400 | 1000 | 100 | |||
200 | 500 | 400 | 200 | 200 | 400 | 500 | 200 |
1000 | 100 | 500 | 400 | 500 | 300 | 200 | 1500 | |||
200 | 1500 | 500 | 300 | 500 | 400 | 400 | 50 | |||
400 | 50 | 500 | 500 | 500 | 500 | 300 | 700 | |||
300 | 700 | 500 | 400 | 500 | 400 | 1000 | 100 | |||
200 | 500 | 400 | 200 | 200 | 400 | 500 | 200 |
0.34 | 0.18 | 0.27 | 0.22 | 0.26 | 0.22 | 0.27 | 0.25 | |||
0.13 | 0.48 | 0.24 | 0.15 | 0.25 | 0.24 | 0.29 | 0.23 | |||
0.24 | 0.28 | 0.27 | 0.21 | 0.25 | 0.24 | 0.36 | 0.15 | |||
0.25 | 0.25 | 0.28 | 0.22 | 0.22 | 0.18 | 0.14 | 0.46 |
0.34 | 0.18 | 0.27 | 0.22 | 0.26 | 0.22 | 0.27 | 0.25 | |||
0.13 | 0.48 | 0.24 | 0.15 | 0.25 | 0.24 | 0.29 | 0.23 | |||
0.24 | 0.28 | 0.27 | 0.21 | 0.25 | 0.24 | 0.36 | 0.15 | |||
0.25 | 0.25 | 0.28 | 0.22 | 0.22 | 0.18 | 0.14 | 0.46 |
471 | 6 | 121 | 83 | 98 | 34 | 19 | 926 | |||
101 | 746 | 159 | 107 | 177 | 114 | 113 | 1 | |||
130 | 1 | 167 | 157 | 114 | 124 | 42 | 256 | |||
44 | 243 | 145 | 85 | 109 | 81 | 489 | 6 | |||
21 | 210 | 114 | 32 | 36 | 119 | 206 | 28 |
471 | 6 | 121 | 83 | 98 | 34 | 19 | 926 | |||
101 | 746 | 159 | 107 | 177 | 114 | 113 | 1 | |||
130 | 1 | 167 | 157 | 114 | 124 | 42 | 256 | |||
44 | 243 | 145 | 85 | 109 | 81 | 489 | 6 | |||
21 | 210 | 114 | 32 | 36 | 119 | 206 | 28 |
total revenue | average revenue | |||
0.3406 | 0.456 | 4313 | 0.4714 | |
0.3391 | 0.564 | 5178 | 0.5659 |
total revenue | average revenue | |||
0.3406 | 0.456 | 4313 | 0.4714 | |
0.3391 | 0.564 | 5178 | 0.5659 |
total profit | average profit | ||||
0.3406 | 0.3406 | 1.3057 | 12016.17 | 1.3132 | |
0.3391 | 0.3391 | 1.8546 | 17072.17 | 1.8658 |
total profit | average profit | ||||
0.3406 | 0.3406 | 1.3057 | 12016.17 | 1.3132 | |
0.3391 | 0.3391 | 1.8546 | 17072.17 | 1.8658 |
total profit | average profit | ||||
0.3406 | 0.3406 | 1.7420 | 15938.17 | 1.7419 | |
0.3391 | 0.3391 | 1.3424 | 12268.17 | 1.3408 |
total profit | average profit | ||||
0.3406 | 0.3406 | 1.7420 | 15938.17 | 1.7419 | |
0.3391 | 0.3391 | 1.3424 | 12268.17 | 1.3408 |
total profit | average profit | ||||
7 | 0.3906 | 3.5381 | 35390 | 3.5390 | |
4 | 0.3882 | 3.8433 | 38771 | 3.8771 | |
4 | 0.3250 | 4.8986 | 48678 | 4.8678 | |
8 | 0.3274 | 3.7050 | 36889 | 3.6889 |
total profit | average profit | ||||
7 | 0.3906 | 3.5381 | 35390 | 3.5390 | |
4 | 0.3882 | 3.8433 | 38771 | 3.8771 | |
4 | 0.3250 | 4.8986 | 48678 | 4.8678 | |
8 | 0.3274 | 3.7050 | 36889 | 3.6889 |
total profit | average profit | ||||
28 | 0.4367 | 1.8682 | 18971 | 1.8971 | |
28 | 0.4025 | 2.1106 | 20746 | 2.0746 | |
56 | 0.4055 | 1.8055 | 17915 | 1.7915 | |
16 | 0.4055 | 2.3585 | 24404 | 2.4404 | |
32 | 0.3385 | 2.0145 | 19903 | 1.9903 |
total profit | average profit | ||||
28 | 0.4367 | 1.8682 | 18971 | 1.8971 | |
28 | 0.4025 | 2.1106 | 20746 | 2.0746 | |
56 | 0.4055 | 1.8055 | 17915 | 1.7915 | |
16 | 0.4055 | 2.3585 | 24404 | 2.4404 | |
32 | 0.3385 | 2.0145 | 19903 | 1.9903 |
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