Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification
Abstract
:1. Introduction and Literature Review
- The original swallow swarm algorithm is designed for continuous optimization; for the first time we offer a new binary version of swallow swarm optimization for solving binary optimization problems.
- A novel feature selection method based on binary swallow swarm optimization is proposed. This is the first work applying the binary swallow swarm optimization to feature selection.
- As an example of building fuzzy rule-based classifiers, the proposed method is compared with wrapper feature selections based on other metaheuristics, feature selection algorithm based on mutual information, and algorithm without feature selection.
- The Wilcoxon signed-rank test is used to evaluate the proposed method.
- A multiple regression equation is found that reflects the relationship between the BSSO runtime and the number of features, the number of instances, and the number of classes.
1.1. Literature Review
1.1.1. Quantum Methods
1.1.2. Modified Algebraic Operations
1.1.3. Transfer Functions
2. Fuzzy Rule-Based Classifier
3. Fuzzy Rule Base Generation
Algorithm 1 Fuzzy rule base generation algorithm. |
1: Input: m, Tr. 2: Output: fuzzy rule base Ri (where i = 1, 2…, m). 3: begin 4: for i←1 to m do 5: for k←1 to D do 6: : 7: ; 8: : 9: ; 10: Calculate a center of : 11: b ← a + (c – a)/2; 12: Create a symmetric triangular membership function with borders a and c, and center b for fuzzy term Aki; 13: end 14: membership functions (where k = 1, 2, …, D) and output ck ← k; 15: end 16: end |
4. Wrapper-Based Feature Selection Algorithm
4.1. Swallow Swarm Optimization
- Head leader is a particle with the best value of the objective function;
- Local leaders are l particles that follow the head leader in accordance with the value of the objective function;
- Aimless particles are k particles with the worst value of the objective function;
- Explorers are all other particles.
4.2. Binary Swallow Swarm Optimization Algorithm
Algorithm 2 Binary Swallow Swarm Optimization. |
1: Input: train data. 2: Output: SHL – position of the head leader. 3: Parameters: iterations – maximum number of cycles, N – population size, D – dimension, pvs, pvhl, phe, pher, ple, pler, l – local leaders, k – aimless particles. 4: begin 5: for i ← to N do 6: Initialize each solution Si in the population: Si ← rand{0,1}D; 7: Select j-th (j = 1,2,…,D) feature for subset Sub where Sij = 1; 8: Build reduced dataset (subtra) based on Sub; 9: Evaluate fitness value (fitnessi) of Sub: fitnessi ← errorrate; Evaluate fitness value (fitnessi) of Sub: fitnessi ← errorrate; 10: end 11: SHL ← Sk, where ; 12: for iter←1 to iterations do 13: for i ← to N do 14: Find nearest local leader SLL among population {S1, S2, …, SN}; 15: Evolve a new solution Se={f1, f2, …, fD} using Equation (2); 16: Select j-th feature (j = 1,2,…,D) for subset Sub, where Sej = 1; 17: Build reduced dataset (subtra) based on Sub; 18: Evaluate fitness value (fitnessi) of Sub: fitnessi ← errorrate; 19: if fitnessSe< fitnessi then 20: Si ← Se; 21: fitnessi ← fitnessSe; 22: end 23: end 24: ; 25: end 26: end |
5. Experiment
5.1. Datasets and Parameter Setting
5.2. Comparison with the Other Approaches
- The proposed method of feature selection on some data sets allows to obtain a classification rate exceeding 90%, which indicates that the feature selection method is effective by reducing the amount of data processing.
- The classification rate of the increases with an increase in the number of features selected. When the number of selected features reaches a certain value, the classification rate decreases.
- The proposed method allows selecting the optimal features.
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Dataset | Number of Classes | Number of Features | Number of Examples |
---|---|---|---|
apendicitis | 2 | 7 | 106 |
balance | 3 | 4 | 625 |
banana | 2 | 2 | 5300 |
bupa | 2 | 6 | 345 |
cleveland | 5 | 13 | 297 |
coil2000 | 2 | 85 | 9822 |
contraceptive | 3 | 9 | 1473 |
dermatology | 6 | 34 | 358 |
ecoli | 8 | 7 | 336 |
glass | 7 | 9 | 214 |
haberman | 2 | 3 | 306 |
heart | 2 | 13 | 270 |
hepatitis | 2 | 19 | 80 |
ionosphere | 2 | 33 | 351 |
iris | 3 | 4 | 150 |
magic | 2 | 10 | 19020 |
newthyroid | 3 | 5 | 215 |
optdigits | 10 | 64 | 5620 |
ring | 2 | 20 | 7400 |
segment | 7 | 19 | 2310 |
spambase | 2 | 57 | 4597 |
tae | 3 | 5 | 151 |
texture | 11 | 40 | 5500 |
thyroid | 3 | 21 | 7200 |
titanic | 2 | 3 | 2201 |
twonorm | 2 | 20 | 7400 |
vehicle | 4 | 18 | 846 |
wdbc | 2 | 30 | 569 |
wine | 3 | 13 | 178 |
wisconsin | 2 | 9 | 683 |
Parameter | Setting |
---|---|
N—Population of particles | 40 |
l—local leaders | 3 |
k—aimless particles | 6 |
Iterations | 300 |
pve | 0.7 |
pvhl | 0.7 |
phe | 0.9 |
pher | 0.9 |
ple | 0.8 |
pler | 0.8 |
Dataset | All features | IG | BSSO | RS | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Learn | Test | F‘ | Learn | Test | F‘ | Learn | Test | F‘ | Learn | Test | |
apendicitis | 72.15 | 66.77 | 4.90 | 79.05 | 78.40 | 3.30 | 83.03 | 79.32 | 3.40 | 83.03 | 80.23 |
balance | 46.24 | 46.25 | 2.00 | 38.88 | 38.90 | 3.60 | 46.26 | 45.93 | 3.50 | 46.26 | 45.77 |
banana | 44.45 | 44.41 | 1.00 | 59.42 | 59.40 | 1.00 | 59.42 | 59.40 | 1.00 | 59.42 | 59.40 |
bupa | 49.47 | 46.92 | 1.70 | 46.60 | 46.89 | 2.90 | 60.48 | 61.15 | 2.90 | 60.48 | 59.98 |
cleveland | 54.07 | 54.04 | 5.00 | 53.58 | 53.05 | 7.70 | 57.02 | 51.63 | 7.20 | 56.95 | 51.28 |
coil2000 | 9.75 | 9.67 | 33.70 | 37.91 | 37.99 | 37.60 | 93.84 | 93.75 | 39.60 | 92.87 | 92.74 |
contraceptive | 42.63 | 42.71 | 5.40 | 42.33 | 42.30 | 2.20 | 44.65 | 44.54 | 2.30 | 44.65 | 44.47 |
dermatology | 93.95 | 89.68 | 13.00 | 41.14 | 38.09 | 20.80 | 97.73 | 92.46 | 19.20 | 97.30 | 92.46 |
ecoli | 32.79 | 32.89 | 3.50 | 50.49 | 51.40 | 3.90 | 53.57 | 53.32 | 3.90 | 53.57 | 53.32 |
glass | 55.81 | 52.65 | 3.30 | 25.31 | 21.62 | 6.00 | 61.00 | 54.02 | 6.30 | 63.54 | 52.71 |
haberman | 45.43 | 45.47 | 2.00 | 44.73 | 44.83 | 1.10 | 70.15 | 70.61 | 1.10 | 70.15 | 70.61 |
heart | 56.87 | 55.93 | 5.10 | 58.26 | 55.92 | 3.60 | 70.41 | 64.07 | 3.50 | 70.04 | 64.07 |
hepatitis | 64.36 | 60.08 | 9.20 | 51.14 | 41.65 | 8.10 | 94.31 | 85.16 | 8.10 | 93.20 | 84.98 |
ionosphere | 87.94 | 87.45 | 9.40 | 67.14 | 67.00 | 7.00 | 85.41 | 81.28 | 8.90 | 78.92 | 74.36 |
iris | 93.85 | 94.00 | 2.00 | 96.74 | 96.67 | 2.10 | 96.89 | 94.67 | 2.00 | 96.89 | 94.67 |
magic | 56.70 | 56.75 | 6.00 | 60.15 | 60.16 | 3.90 | 71.39 | 71.44 | 3.90 | 71.39 | 71.44 |
newthyroid | 96.54 | 95.39 | 3.00 | 94.89 | 94.39 | 3.40 | 97.88 | 95.84 | 3.50 | 97.88 | 95.37 |
optdigits | 23.41 | 23.12 | 22.30 | 24.46 | 24.10 | 37.80 | 47.32 | 46.00 | 35.60 | 42.93 | 41.72 |
ring | 49.51 | 49.52 | 10.20 | 49.17 | 49.19 | 1.00 | 58.50 | 58.44 | 4.60 | 54.52 | 53.20 |
segment | 72.20 | 71.48 | 6.00 | 20.31 | 20.09 | 8.30 | 88.26 | 86.02 | 8.80 | 87.78 | 85.46 |
spambase | 56.81 | 56.73 | 27.90 | 63.84 | 64.29 | 22.60 | 73.57 | 73.45 | 25.10 | 70.93 | 70.74 |
tae | 35.17 | 36.41 | 3.50 | 34.66 | 34.41 | 2.80 | 41.80 | 41.07 | 3.00 | 41.80 | 40.40 |
texture | 64.48 | 68.55 | 19.10 | 27.88 | 27.84 | 13.00 | 68.49 | 68.31 | 16.00 | 66.85 | 66.09 |
thyroid | 22.50 | 22.50 | 17.30 | 92.31 | 92.36 | 1.50 | 93.33 | 93.24 | 1.80 | 88.38 | 89.14 |
titanic | 68.53 | 66.52 | 1.00 | 77.61 | 77.87 | 1.50 | 77.61 | 77.87 | 1.50 | 77.61 | 77.87 |
twonorm | 96.87 | 96.91 | 10.10 | 88.32 | 88.20 | 19.70 | 96.89 | 96.91 | 19.90 | 96.67 | 95.68 |
vehicle | 25.40 | 24.83 | 8.00 | 21.93 | 21.75 | 8.10 | 49.24 | 43.74 | 7.70 | 47.47 | 45.88 |
wdbc | 90.43 | 90.17 | 18.50 | 93.40 | 92.97 | 10.90 | 97.34 | 96.12 | 11.70 | 96.09 | 95.78 |
wine | 89.82 | 87.57 | 6.10 | 50.01 | 45.36 | 6.50 | 96.57 | 94.41 | 7.00 | 95.86 | 94.31 |
wisconsin | 92.81 | 92.25 | 6.30 | 75.68 | 74.98 | 5.60 | 94.42 | 93.85 | 7.10 | 94.63 | 92.75 |
Average | 59.70 | 58.92 | 8.88 | 55.58 | 54.74 | 8.58 | 74.23 | 72.27 | 9.00 | 73.27 | 71.23 |
Dataset | BMA | BGA(S) | BGA(V) | BBA(S) | BBA(V) | |||||
---|---|---|---|---|---|---|---|---|---|---|
Test | F‘ | Test | F‘ | Test | F‘ | Test | F‘ | Test | F‘ | |
apendicitis | 76.36 | 2.90 | Nr 1 | nr | nr | nr | 78.76 | 3.10 | 77.11 | 3.40 |
balance | 46.24 | 4.00 | nr | nr | nr | nr | 46.08 | 3.04 | 46.08 | 3.04 |
banana | 59.36 | 1.00 | nr | nr | nr | nr | nr | nr | nr | nr |
bupa | 58.17 | 2.70 | 59.80 | 2.80 | 60.00 | 2.70 | 57.30 | 2.74 | 56.56 | 2.66 |
cleveland | 54.15 | 8.20 | 52.50 | 7.30 | 54.40 | 2.80 | 53.30 | 6.40 | 52.00 | 8.36 |
coil2000 | 74.96 | 31.3 | 90.60 | 38.5 | 94.00 | 1.00 | nr | nr | nr | nr |
contraceptive | 44.06 | 2.80 | nr | nr | nr | nr | nr | nr | nr | nr |
dermatology | 90.99 | 21.6 | nr | nr | nr | nr | nr | nr | nr | nr |
ecoli | 51.22 | 3.90 | nr | nr | nr | nr | 50.30 | 3.34 | 50.90 | 3.48 |
glass | 56.12 | 6.20 | 56.00 | 5.10 | 53.20 | 5.90 | 57.60 | 5.30 | 55.40 | 5.73 |
haberman | 67.96 | 1.10 | nr | nr | nr | nr | 57.50 | 1.90 | 57.60 | 1.90 |
heart | 74.21 | 5.40 | 67.00 | 2.80 | 67.70 | 3.00 | 66.60 | 3.36 | 66.70 | 4.20 |
hepatitis | 85.51 | 8.10 | 87.20 | 7.90 | 82.50 | 5.30 | 84.52 | 9.40 | 81.22 | 9.17 |
ionosphere | 91.58 | 16.8 | nr | nr | nr | nr | 89.29 | 16.4 | 89.81 | 18.2 |
iris | 94.67 | 1.80 | nr | nr | nr | nr | 95.07 | 1.92 | 94.80 | 1.90 |
magic | nr | nr | 70.70 | 4.10 | 70.70 | 4.10 | 71.41 | 3.90 | 70.41 | 4.32 |
newthyroid | 96.77 | 3.50 | 96.50 | 3.70 | 96.50 | 3.30 | 93.67 | 4.15 | 94.02 | 4.20 |
optdigits | nr | nr | nr | nr | nr | nr | nr | nr | nr | nr |
ring | 58.39 | 1.00 | 58.60 | 1.00 | 57.90 | 1.00 | nr | nr | nr | nr |
segment | 81.90 | 9.00 | 85.70 | 9.10 | 84.10 | 8.80 | nr | nr | nr | nr |
spambase | 74.27 | 23.7 | 65.40 | 27.0 | 70.00 | 2.70 | nr | nr | nr | nr |
tae | nr | nr | nr | nr | nr | nr | nr | nr | nr | nr |
texture | 73.69 | 12.1 | nr | nr | nr | nr | nr | nr | nr | nr |
thyroid | 90.93 | 1.80 | 99.30 | 20.0 | 99.30 | 16.9 | nr | nr | nr | nr |
titanic | 77.60 | 1.40 | nr | nr | nr | nr | nr | nr | nr | nr |
twonorm | 96.74 | 19.7 | 96.80 | 19.9 | 96.10 | 17.8 | nr | nr | nr | nr |
vehicle | 47.87 | 5.90 | 45.60 | 7.80 | 40.00 | 4.80 | 43.05 | 6.16 | 40.55 | 6.56 |
wdbc | 95.18 | 10.4 | nr | nr | nr | nr | nr | nr | nr | nr |
wine | 96.08 | 6.20 | 94.80 | 5.80 | 92.20 | 6.80 | 92.39 | 6.28 | 93.56 | 6.74 |
wisconsin | 93.59 | 5.30 | 94.00 | 5.70 | 93.60 | 3.50 | nr | nr | nr | nr |
Median of Differences | Standardized Test Statistic | p-Value | Null Hypothesis |
---|---|---|---|
BSSO_Learn—All_Learn | 4.659 | <0.001 | Reject |
BSSO_Learn—IG_Learn | 4.623 | <0.001 | Reject |
BSSO_Learn—RS_Learn | 3.070 | 0.002 | Reject |
BSSO_Test—All_Test | 4.206 | <0.000 | Reject |
BSSO_Test—IG_Test | 4.418 | <0.000 | Reject |
BSSO_Test—RS_Test | 3.263 | 0.001 | Reject |
BSSO_Test—BSMA_Test | 0.127 | 0.899 | Retain |
BSSO_Test—BGSA(S)_Test | −0.379 | 0.879 | Retain |
BSSO_Test—BGSA(V)_Test | 1.060 | 0.299 | Retain |
BSSO_Test—BBSO(S)_Test | 0.682 | 0.496 | Retain |
BSSO_Test—BBSO(V)_Test | 1.363 | 0.173 | Retain |
BSSO_F‘—IG_F‘ | −0.389 | 0.697 | Retain |
BSSO_F‘—RS_F‘ | −2.027 | 0.043 | Reject |
BSSO_F‘—BSMA_F‘ | −0.522 | 0.602 | Retain |
BSSO_F‘—BGSA(S)_F‘ | −0.455 | 0.649 | Retain |
BSSO_F‘—BGSA(V)_F‘ | 1.847 | 0.065 | Retain |
BSSO_F‘—BBSO(S) _F | −0.502 | 0.615 | Retain |
BSSO_F‘—BBSO(V) _F | −1.193 | 0.233 | Retain |
Dataset | BSSO | RS | IG |
---|---|---|---|
apendicitis | 2.4969 | 1.7509 | 0.0690 |
balance | 8.6825 | 7.2545 | 0.0909 |
banana | 28.8653 | 26.7955 | 0.1769 |
bupa | 4.3853 | 4.0395 | 0.0807 |
cleveland | 13.7823 | 10.5905 | 0.1189 |
coil2000 | 649.0763 | 502.0300 | 115.3146 |
contraceptive | 25.6143 | 23.5633 | 0.2409 |
dermatology | 34.5858 | 30.0775 | 0.2775 |
ecoli | 12.4913 | 12.1631 | 0.1179 |
glass | 7.7928 | 7.5788 | 0.1019 |
haberman | 2.7073 | 2.3386 | 0.0730 |
heart | 5.3957 | 3.7275 | 0.0929 |
hepatitis | 2.5654 | 2.1653 | 0.0760 |
ionosphere | 11.6846 | 11.0012 | 0.1689 |
iris | 2.2396 | 2.0328 | 0.0700 |
magic | 210.2207 | 186.0533 | 6.4670 |
newthyroid | 3.9593 | 3.8287 | 0.0780 |
optdigits | 1355.8079 | 1300.9188 | 26.0010 |
ring | 135.9984 | 75.0970 | 2.5237 |
segment | 134.1489 | 124.1207 | 1.0419 |
spambase | 213.8056 | 145.8274 | 11.2431 |
tae | 2.4275 | 2.4025 | 0.0710 |
texture | 980.7259 | 679.4120 | 12.0827 |
thyroid | 200.5548 | 113.6152 | 4.5406 |
titanic | 10.3387 | 9.7130 | 0.1179 |
twonorm | 200.8571 | 141.2837 | 4.1145 |
vehicle | 29.2830 | 26.9224 | 0.2696 |
wdbc | 16.3909 | 14.0634 | 0.2389 |
wine | 4.9080 | 4.8560 | 0.0879 |
wisconsin | 8.6997 | 8.2846 | 0.1189 |
Model | Unstandardized Coefficients | t | Sig. Level | 95.0% Confidence Interval for B | ||||
---|---|---|---|---|---|---|---|---|
B | Std. Error | Lower Bound | Upper Bound | |||||
(Constant) | −285.890 | 54.884 | −5.209 | 0.000 | −398.707 | −173.074 | ||
NoCl | 60.487 | 11.764 | 5.142 | 0.000 | 36.306 | 84.667 | ||
NoFe | 7.682 | 1.607 | 4.780 | 0.000 | 4.378 | 10.985 | ||
NoEx | 0.022 | 0.007 | 2.975 | 0.006 | 0.007 | 0.037 |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Hodashinsky, I.; Sarin, K.; Shelupanov, A.; Slezkin, A. Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification. Symmetry 2019, 11, 1423. https://doi.org/10.3390/sym11111423
Hodashinsky I, Sarin K, Shelupanov A, Slezkin A. Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification. Symmetry. 2019; 11(11):1423. https://doi.org/10.3390/sym11111423
Chicago/Turabian StyleHodashinsky, Ilya, Konstantin Sarin, Alexander Shelupanov, and Artem Slezkin. 2019. "Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification" Symmetry 11, no. 11: 1423. https://doi.org/10.3390/sym11111423