Concept Drift Adaptation with Incremental–Decremental SVM
Abstract
:1. Introduction
2. Related Work: Concept Drift with Adaptive Shifting Windows
3. Background: The Adaptive Window Model for Drift Detection and the Incremental–Decremental SVM
3.1. Concept Drift with Adaptive Window
3.2. Kuhn–Tucker Conditions and Vector Migration in Incremental–Decremental SVMs
4. Adaptive-Window Incremental–Decremental SVM (AIDSVM)
Algorithm 1 Concept drift AIDSVM learning and unlearning |
|
- 1.
- Perform an O(1) test to check if the associated is within the allowed limits, , while testing whether the penalty has either reached zero (due to migrating to support set) or a positive or negative value (due to migration to the rest/error sets);
- 2.
- Computation of Q is in ;
- 3.
- Computation of , given by Equation (11), is based on matrix inversion, so it is in , where is the number of support vectors;
- 4.
- Computation of is in as given by Equation (12);
- 5.
- Procedure compute_limits_for_support_and_rest_vectors() is in , computation of the maximum/minimum for values associated to support vectors is in , and for the rest vectors we have to compute the penalties h, which is ;
- 6.
- Procedure reassign_vectors_in_sets() has linear time.
5. Experiments
5.1. SINE1 Dataset
5.2. CIRCLES Dataset
5.3. COVERTYPE Dataset
5.4. Performance Comparison
5.5. Qualitative Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Dataset | Window Size | (gamma) | C |
---|---|---|---|
SINE1 | 1500 | 6.008484 | 10 |
CIRCLES | 1000 | 7.797753 | 1 |
COVERTYPE | 2000 | 0.241477 | 100 |
Method on Dataset | SINE1 | CIRCLES | COVERTYPE |
---|---|---|---|
NB+FHDDM | 85.32% | 86.24% | 87.94% |
NB+FHDDMS | 85.35% | 86.26% | 87.05% |
NB+DDM | 85.06% | 86.06% | 88.43% |
NB+EDDM | 82.50% | 84.81% | 84.92% |
NB+ADWIN | 84.72% | 86.20% | 86.78% |
HT+FHDDM | 86.37% | 87.16% | 89.16% |
HT+FHDDMS | 86.36% | 87.19% | 87.58% |
HT+DDM | 86.09% | 86.97% | 89.90% |
HT+EDDM | 83.69% | 85.21% | 84.98% |
HT+ADWIN | 84.09% | 86.74% | 87.02% |
C-SVM | 84.83% | 87.17% | 91.79% |
AIDSVM | 88.68% | 87.22% | 92.17% |
Method on Dataset | SINE1 | CIRCLES | COVERTYPE |
---|---|---|---|
C-SVM | 154 ± 12 | 115 ± 8 | 119 ± 11 |
AIDSVM | 117 ± 7 | 75 ± 9 | 73 ± 6 |
Drifts Signalled | SINE1 | CIRCLES | COVERTYPE |
---|---|---|---|
NB+FHDDM | 20,048, 40,043, 60,048, 80,047 | 25,061, 50,063, 75,104 | 803, 1761, 2689, 3149, 3587 ... |
NB+FHDDMS | 20,033, 40,035, 60,047, 80,037 | 25,061, 50,023, 75,104 | 803, 1607, 2009, 2644, 3129 ... |
NB+DDM | 20,156, 40,138, 60,106, 80,171 | 25,339, 50,240, 75,676 | 839, 2105, 2717, 3149, 3588 ... |
NB+EDDM | 93, 21,121, 40,949, 61,038, 61,165 ... | 110, 260, 31,163, 50,397, 50,629 ... | 116, 280, 364, 553, 703 ... |
NB+ADWIN | 20,065, 26,178, 27,775, 29,924, 40,069 ... | 9537, 25,090, 27,843, 50,052, 75,205 ... | 1025, 2818, 3747, 4772, 5637 ... |
HT+FHDDM | 20,054, 40,052, 41,756, 60,052, 80,051 | 25,061, 50,063, 75,066 | 816, 1673, 2031, 2700, 3146 ... |
HT+FHDDMS | 20,036, 40,042, 41,765, 60,047, 80,038 | 25,061, 50,023, 75,066 | 816, 1607, 2009, 2706, 3123 ... |
HT+DDM | 20,150, 40,144, 60,154, 80,164 | 25,304, 50,297, 75,559 | 853, 1924, 51,532, 51,741, 51,775 ... |
HT+EDDM | 93, 20,899, 40,873, 60,951, 61,224 ... | 110, 260, 27,840, 31,500, 31,930 ... | 116, 280, 364, 553, 703 ... |
HT+ADWIN | 2877, 4770, 6435, 8260, 12,869 ... | 1985, 5506, 25,091, 28,420, 50,053 ... | 993, 2786, 4675, 5636, 5797 ... |
AIDSVM | 20,036, 40,041, 60,044, 80,038 | 25,055, 25,069, 50,023, 50,030, 50,037, 75,668 ... | 990, 996, 1006, 4715, 4751 ... |
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Gâlmeanu, H.; Andonie, R. Concept Drift Adaptation with Incremental–Decremental SVM. Appl. Sci. 2021, 11, 9644. https://doi.org/10.3390/app11209644
Gâlmeanu H, Andonie R. Concept Drift Adaptation with Incremental–Decremental SVM. Applied Sciences. 2021; 11(20):9644. https://doi.org/10.3390/app11209644
Chicago/Turabian StyleGâlmeanu, Honorius, and Răzvan Andonie. 2021. "Concept Drift Adaptation with Incremental–Decremental SVM" Applied Sciences 11, no. 20: 9644. https://doi.org/10.3390/app11209644
APA StyleGâlmeanu, H., & Andonie, R. (2021). Concept Drift Adaptation with Incremental–Decremental SVM. Applied Sciences, 11(20), 9644. https://doi.org/10.3390/app11209644