Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests
Abstract
:1. Introduction
2. Related Work
3. Materials and Methods
3.1. Crisp and Fuzzy Discretization
3.2. Type of Fuzzy Membership Function
3.3. Multinomial Naïve Bayes
4. Empirical Application
4.1. Description and Exploration of Dataset
4.2. Modelings
4.3. Result and Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Class | Mean of Pixel Value in Channel | Std. Dev. of Pixel Value in Channel | ||||
---|---|---|---|---|---|---|
Red | Green | Blue | Red | Green | Blue | |
NP | 129.13 | 157.58 | 118.20 | 28.55 | 26.63 | 29.39 |
LRD | 125.19 | 134.25 | 102.63 | 19.10 | 20.38 | 18.61 |
DWD | 152.88 | 146.40 | 51.40 | 9.26 | 14.24 | 21.46 |
LBD | 117.03 | 122.94 | 101.62 | 13.81 | 12.83 | 15.87 |
LP | 195.71 | 199.24 | 134.67 | 19.01 | 17.46 | 38.97 |
SFP | 120.58 | 150.04 | 64.54 | 13.36 | 12.04 | 30.37 |
HAP | 163.26 | 149.15 | 89.35 | 16.53 | 11.16 | 14.12 |
Henze-Zirkler Test | Class of Corn Diseases and Pests | ||||||
---|---|---|---|---|---|---|---|
LRD | DWD | LBD | LP | SFP | HAP | NP | |
Statistic | 5.55 | 1.67 | 1.89 | 2.31 | 11.68 | 4.49 | 18.89 |
p-value | 0.00 | 7.2 × 10−6 | 2.56 × 10−7 | 1.64 × 10−9 | 0.00 | 0.00 | 0.00 |
1 | [32.77, 75.55] | [63.00, 99.70] | [12.18, 56.11] |
2 | [75.56, 118.34] | [99.71, 136.40] | [56.12, 100.03] |
3 | [118.35, 161.12] | [136.41, 173.10] | [100.04, 143.95] |
4 | [161.13, 203.90] | [173.11, 209.80] | [143.96, 187.87] |
5 | [203.91, 246.68] | [209.81, 246.50] | [187.88, 231.80] |
Model | ||||
---|---|---|---|---|
FMNB1 | 1 | [32.77, 57.64, 85.55] | [63.00, 83.41, 109.70] | [12.18, 34.74, 66.11] |
2 | [72.64, 66.54, 128.34] | [93.41, 118.42, 146.40] | [49.74, 74.79, 110.03] | |
3 | [102.64, 137.62, 171.12] | [131.45, 156.43, 183.10] | [89.79, 119.80, 153,95] | |
4 | [152.64,181.60, 213.90] | [168.47, 188.46,, 219.80] | [134.81, 167,81, 197.87] | |
5 | [206.02, 220.04, 246.68] | [175.20, 189.23, 246.50] | [192.29, 206.33,231.80] | |
FMNB2 | 1 | [32.77,44.77, 62.77, 90.55] | [63.00, 78.00, 93.00, 114.70] | [12.18, 27.17, 42.18, 71.11] |
2 | [55.77, 75.76, 97.77, 133.34] | [86.00, 106.00, 128.00, 151.40] | [35.18,55.16, 77.18, 115.03] | |
3 | [83.77, 103.76, 125.77, 176.12] | [114.00, 106.00, 128.00, 188.10] | [63.18, 83.17, 105.17, 158.95] | |
4 | [111.76, 131.77, 153.76, 218.90] | [142.00, 162.00, 184.01, 224.80] | [91.18, 111.17, 133.18, 202.87] | |
5 | [139.77, 159.76, 181.76, 246.68] | [170.00, 190.02, 212.01, 246.50] | [119.18, 139.17, 161.16, 231.80] | |
FMNB3 | 1 | [32.77, 88.64] | [63.00, 113.41] | [12.18, 66.74] |
2 | [51.54, 93.06, 135.57] | [65.10,92.09, 155.08] | [30.03, 78.91, 120.79] | |
3 | [114.00, 127.85, 174.68] | [105.37, 153.79, 195.21] | [80.10, 125.17, 160.24] | |
4 | [143.11,150.44, 225.77] | [164.28, 179.19, 200.09] | [145.19, 182.06, 198.93] | |
5 | [206.02, 220.04, 246.68] | [175.20, 246.50] | [192.29, 231.80] | |
FMNB4 | 1 | [32.77, 88.64] | [63.00, 113.41] | [12.18, 66.74] |
2 | [96.64, 116.64, 138.63, 133.34] | [121.41, 141.42, 163.44, 151.40] | [74.74, 94.70, 116.73, 115.03] | |
3 | [124.64, 144.63, 166.62, 176.12] | [149.41, 169.44, 169.42, 188.10] | [102.74, 122.72, 144.70, 158.95] | |
4 | [152.64, 172.62, 194.64, 218.90] | [177.41, 197.42, 219.40, 224.80] | [130.74, 150.72, 172.71, 202.87] | |
5 | [206.02, 246.68] | [175.20, 246.50] | [192.29, 231.80] | |
FMNB5 | 1 | [32.77, 60.64, 88.50] | [63.00, 82.41, 101.81] | [12.18, 43.74, 75.30] |
2 | [71.54, 96.06, 120.57] | [85.10, 115.09, 145.08] | [56.03, 87.91, 119.79] | |
3 | [109.00, 127.85, 146.68] | [109.37, 137.79, 166.21] | [99.10, 128.17, 157.24] | |
4 | [131.11, 166.44, 201.77] | [135.28, 171.19, 207.09] | [143.19, 171.06, 198.93] | |
5 | [170.02, 208.35, 246.68] | [169.20, 207.85, 246.50] | [187.29, 209.55, 231.80] | |
FMNB6 | 1 | [32.77, 60.64, 88.50] | [63.00, 82.41, 101.81] | [12.18, 43.74, 75.30] |
2 | [71.54, 87.50, 106.15, 120.57] | [85.10, 109.55, 126.18, 145.08] | [56.03, 68.62, 87.58, 119.79] | |
3 | [109.00, 126.41, 139.04, 146.68] | [109.37, 146.44, 163.07, 166.21] | [99.10, 112.51, 131.47, 157.24] | |
4 | [131.11, 150.30, 181.93, 201.77] | [135.28, 183.33, 199.96, 207.09] | [137.44, 156.40, 175.36, 194.33] | |
5 | [170.02, 208.35, 246.68] | [169.20, 207.85, 246.50] | [187.29, 209.55, 231.80] |
Metrics | Source of Var. | Sum of Squares | Mean Squares | F | p-Value | F-Criteria |
---|---|---|---|---|---|---|
Accuracy | between | 9.14 | 2.29 | 876.63 | 2 × 10−100 | 2.43 |
within | 0.38 | 0.00 | ||||
Precision | between | 441.47 | 110.37 | 790.85 | 2.37 × 10−97 | |
within | 20.24 | 0.14 | ||||
Recall | between | 621.85 | 155.46 | 703.45 | 7.73 × 10−94 | |
within | 32.05 | 0.22 | ||||
Fscore | between | 425.96 | 106.49 | 853.80 | 1.2 × 10−99 | |
within | 18.08 | 0.12 | ||||
AUC | between | 96.41 | 24.10 | 44.09 | 3.57 × 10−24 | |
within | 79.27 | 0.55 | ||||
Kappa | between | 225.68 | 56.42 | 962.04 | 3 × 10−103 | |
within | 8.50 | 0.06 |
Comparison Model | Absolute Mean Difference | |||||
---|---|---|---|---|---|---|
Accuracy | Precision | Recall | Fscore | AUC | Kappa | |
MNB vs. FMNB2 | 0.57 | 1.41 | 2.94 | 2.27 | 0.83 | 3.01 |
MNB vs. FMNB3 | 0.70 | 0.85 | 3.95 | 2.57 | 2.29 | 3.52 |
MNB vs. FMNB5 | 0.57 | 0.96 | 4.36 | 1.96 | 1.88 | 2.65 |
MNB vs. FMNB6 | 0.35 | 3.47 | 0.57 | 1.86 | 1.27 | 1.91 |
FMNB2 vs. FMNB3 | 0.12 | 0.56 | 1.01 | 0.31 | 1.46 | 0.52 |
FMNB2 vs. FMNB3 | 0.00 | 2.36 | 1.42 | 0.31 | 1.05 | 0.36 |
FMNB2 vs. FMNB3 | 0.23 | 4.88 | 3.50 | 4.13 | 0.44 | 1.09 |
FMNB3 vs. FMNB5 | 0.12 | 1.81 | 0.41 | 0.61 | 0.41 | 0.88 |
FMNB3 vs. FMNB6 | 0.35 | 4.32 | 4.51 | 4.43 | 1.02 | 1.61 |
FMNB5 vs. FMNB6 | 0.23 | 2.51 | 4.92 | 3.82 | 0.61 | 0.73 |
Prediction Method/ Dataset/Number of Class | Combination of Fuzzy Membership Functions | Performance of Original Model (%) | Performance of Fuzzy Approach Model (%) | ||||||
---|---|---|---|---|---|---|---|---|---|
Accu | Prec | Rec | Spec | Accu | Prec | Rec | Spec | ||
NN-RF/breast cancer SA/two (Algehyne et al. [4]) | all trapezoidal | 95.61 | - | 88.45 | 93.56 | 99.33 | - | 99.41 | 99.24 |
GA/driver behavior (Fernandez et al. [5]) | linear and triangular trapezoidal (only for age) | - | - | - | - | - | 82.00 | 87.00 | - |
DTID3/Corn Diseases and Pests (Resti et al. [9]) | S and triangular | 94.53 | 84.31 | 83.07 | 96.72 | 97.76 | 89.83 | 94.87 | 98.66 |
NB/ Heart Disease Status (Femina and Sudheep [10]) | all triangular | 87.60 | - | 88.63 | 79.10 | 91.63 | - | 92.68 | 90.19 |
NB/ Types of Cans Waste (Resti et al. [11]) | linear and triangular | 50.26 | - | - | - | 85.19 | - | - | - |
NB/heart disease (Yazgi and Necla [12]) | all trapezoidal | 74.00 | - | - | - | 81.50 | - | - | - |
Proposed method | linear and triangular | 97.93 | 93.29 | 81.99 | 98.79 | 98.63 | 94.14 | 85.94 | 99.21 |
Paper | No of Class | No of Obs. | Evaluation Method | Classification Method | Performance Metric (%) | |||
---|---|---|---|---|---|---|---|---|
Accu | Prec | Rec | Fscore | |||||
Panigrahi et al. [15] | 2 | 3823 | Hold out with a ratio of 90:10 | DT | 74.35 | - | 75.00 | 75.00 |
KNN | 76.16 | - | 75.00 | 76.00 | ||||
NB | 77.46 | - | 78.00 | 75.50 | ||||
SVM | 77.56 | - | 78.50 | 78.50 | ||||
RF | 79.23 | - | 79.00 | 81.50 | ||||
Kusumo et al. [16] | 4 | 3852 | 10-CV | DT | 77.00 | - | - | - |
NB | 78.00 | - | - | - | ||||
RF | 88.00 | - | - | - | ||||
SVML | 89.00 | - | - | - | ||||
SVMR | 87.00 | - | - | - | ||||
Sibiya and Sumbwanyambe [18] | 4 | 100 | Hold out with a ratio of 70:30 | CNN | 92.85 | - | - | - |
Haque [19] | 4 | 5939 | Hold out with a ratio of 70:30 | CNN | 95.71 | - | - | - |
Syarief and Setiawan [17] | 4 | 200 | 10-CV | DT | 83.30 | - | 83.58 | - |
KNN | 93.30 | - | 94.72 | - | ||||
SVM | 93.50 | - | 95.08 | - | ||||
Resti et al. [14] | 6 | 761 | Hold out with a ratio of 80:20 | MNB | 92.72 | 79.88 | 79.24 | 78.17 |
KNN | 98.54 | 88.57 | 94.38 | 93.59 | ||||
Resti et al. [9] | 6 | 761 | 10-CV | DT | 94.53 | 84.31 | 83.07 | 83.58 |
FDT | 97.76 | 89.39 | 94.87 | 93.29 | ||||
Resti et al. [50] | 7 | 4616 | 5-CV | LR | 99.85 | 98.59 | 98.15 | 98.37 |
Proposed Method | 7 | 3172 | Monte Carlo | MNB | 97.93 | 93.29 | 81.99 | 87.28 |
FNB3 | 98.63 | 94.14 | 85.94 | 89.95 |
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Resti, Y.; Irsan, C.; Neardiaty, A.; Annabila, C.; Yani, I. Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests. Mathematics 2023, 11, 1761. https://doi.org/10.3390/math11081761
Resti Y, Irsan C, Neardiaty A, Annabila C, Yani I. Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests. Mathematics. 2023; 11(8):1761. https://doi.org/10.3390/math11081761
Chicago/Turabian StyleResti, Yulia, Chandra Irsan, Adinda Neardiaty, Choirunnisa Annabila, and Irsyadi Yani. 2023. "Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests" Mathematics 11, no. 8: 1761. https://doi.org/10.3390/math11081761
APA StyleResti, Y., Irsan, C., Neardiaty, A., Annabila, C., & Yani, I. (2023). Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests. Mathematics, 11(8), 1761. https://doi.org/10.3390/math11081761