เอกสารนี้ตีพิมพ์ในประมวลการประชุมวิชาการระดับชาติ ครั้งที่ 2 (The 2 nd. National Conference of Academic of All Business. Disciplines Research) มหาวิทยาลัยบูรพา

1. ˆ F F F F
F
The Effect of Weight Parameter on Accuracy of Classification by Neural Networks
F
ˆ F F F F
F F F
ˆ F F F F F F
F F F F F F
F F F F F
F F F F F
F F F F F F
F F F F F F F
F F F F +0.1 +1 F ˈ
F F F F ˈ F
F F F F F
: F , ,
Abstract
Now a day the computer neural network system becomes an important part
of industrial, experimental and business software. The purpose of this experiment was
to find the effect of weight parameters on accuracy of artificial neural network in
classification task. In most previous researches on neural network, there was no
mention of the importance of weight parameter values. There were no focused
researches about the effect of weight setting in neural network.
The result was the manual weight randomization method provided higher
accuracy than the automatic weight randomization method, and the best manual
weight parameter was between +0.1 and +1. The result of this independent study was
summarized into knowledge for further research.
Keywords: Artificial Neural Network, Data Classification, Decision Support System

2. F F F F ˆ F F
F ˈ F F F F F
F ˈ F F F
F ˆ F (Business Intelligent System)
F F F F
(Artificial Neural Network) ˈ F F ˆ F F F
F
F F F ˈ
F F F F (Data Classification)
F F F F , (Pattern Recognition)
F , F (Forecasting)
F F ˈ F
F F F F
F F F F F
˂ F ˆ F F F F F F
F F F F
ˈ F F F F F ˈ F F
F F F F F F
F F
F
F
1. F F F
2. F F F F F F
F
3. F F F F F F
4. F F
F F F F

3. F F
F (Process of supervised machine learning) Kotsiantis (2007)
1. F F
F F F ˆ F
F (Multi-Layered Perceptron) F F F F (Feed
Forward) F ˈ 3 F F F
(Input Layer) F (Hidden Layer) (Output Layered) 1
1: F 3
F F (Nodes) F
Attributes F F F F (Hidden Layer)
Rule of Thumb (Number of
inputs + outputs) * (2/3) (Output Layer) F
(Class) F
1: F F
Layer Type Nodes Adjust Method
Input Layer Number of Dataset Attribute
Hidden Layer Rule of Thumb
Output Layer Number of Dataset Class

4. F F F F
F F F F ( 2 4)
2: F F F Iris
3: F F F Wine

5. 4: F F F Zoo
F F F F F Iris
F 4 F F 5 F
F 3 F F F Wine F 13 F
F 11 F F 3 F F Zoo
F 16 F F 15 F F 7
F F F F
Alyuda Neuro Intelligent

6. 2. F F ʿ
F F UCI Machine Learning Repository
3 F F Iris Wine Zoo ˈ F F
(Intelligent Systems) F F
F Iris ˈ F Iris 3 F F Setosa Virsicolor
Verginica F (Attribute) 4 F F
F F F F
150 F
2: F Iris
Associated Tasks: Number of Class
Number of
Instances:
Number of
Attributes:
Classification 3 150 4
F Wine ˈ F 3 F F Wine A, Wine B
Wine C F 13 F F Alcohol, Malic acid, Ash, Alcalinity
of ash, Magnesium, Total phenols, Flavanoids, Nonflavanoid phenols, Proanthocyanins, Color
intensity, Hue, Proline OD280/OD315 of diluted wines F F 178
F
3: F Wine
Associated Tasks: Number of Class
Number of
Instances:
Number of
Attributes:
Classification 3 178 13

7. F Zoo ˈ F F 7 F F 16
F F hairs, feathers, eggs, milk, airborne, aquatic, predator, toothed, backbone,
breathes, venomous, fins, legs, tail, domestic catsize F F 101
F
4: F Zoo
Associated Tasks: Number of Class
Number of
Instances:
Number of
Attributes:
Classification 7 101 16
F F F F ˈ CSV F
F F True False ˈ 0 1
F F F F ˈ F 10 K-Fold Cross
Validation 1 Fold F 1 1
8 F
3. F F
˂ F F F
F F F
(Automatic Weight) F F F (Manual Weight) F
F F F F F
F F
F F F +0.1 +10 F F F
F F F F CCR (Correct Classification Rate) F
F F 10 Fold F F F F
F F F 10 F F F F F F F
F F CCR F F F
F F F F F F F F
F F CCR F F 5

8. 5: F F F +0.1 +10 F F F
Weight
Correct Classification Rate (%)
Iris Wine Zoo
0.1 96.43 100 94.95
0.2 96.08 99.84 86.87
0.3 94 99.84 88.89
0.4 96.41 99.75 85.75
0.5 96.50 99.43 92.93
0.6 96.31 99.75 85.86
0.7 96.59 99.67 89.9
0.8 95.85 99.69 78.79
0.9 96.50 99.84 79.80
1 96.04 100 74.75
2 90.01 99.10 64.65
3 76.69 90.41 63.64
4 66.35 88.52 53.54
5 72.59 83.56 44.44
6 73.76 73.96 42.42
7 68.53 70.49 44.45
8 59.34 76.66 43.43
9 70.80 73.65 39.39
10 65.41 70.89 35.35
Maximum CCR 96.59 100 94.95
5 F F F F F F F
F +0.1 +10 F 5 ˈ F 5

9. 0
20
40
60
80
100
0.1
0.3
0.5
0.7
0.9
2
4
6
8
10
Weight
Percent Iris
Wine
Zoo
5: F F Iris, Wine Zoo
5 F F F F F 3 F
F +0.1 +10 F F F F F F F
F F F +1 F
F F F F F F F F F +0.1 +1
F
4. F F F F
F F F ˈ F
F F F F F +0.1 +1 F F F
F F F F
F F 3 10 Fold ˂ F F
F F F F F 8 F Fold F
F 2 F F F
˂ F Fold 10 (1 Fold = 10 F 1 10 Fold F
˂ F 100 ) F F Fold F F
10 Fold F 10 ˈ F F CCR F F
(Automatic CCR)

10. F F Automatic CCR F F F
F F F F F +0.1 F F 3 ˈ
F Automatic CCR ˂ F F F
F F F F 8 F Fold F
F 2 F F F
˂ F Fold 10 (1 Fold = 10 F 1 10 Fold F
˂ F 100 ) F F Fold F F
10 Fold F 10 ˈ F F (Manual CCR) F
+0.1 F F F F Manual CCR F +1
F F Manual CCR 10 F F F F (Maximum CCR)
Automatic CCR F 3
F F
F F F F F F F
Maximum CCR F Automatic CCR ˈ 6
6: Iris, Wine Zoo F Manual Automatic
Weight
Correct Classification Rate
Iris Wine Zoo
0.1 95.54 99.64 96.3
0.2 96.4 99.9 95.25
0.3 96.77 99.89 95.63
0.4 96.4 99.89 95.51
0.5 96.78 99.81 95.86
0.6 96.8 99.76 94.38
0.7 96.82 99.83 93.14
0.8 96.75 99.73 91.62
0.9 96.78 99.66 87.18
1 96.25 99.77 88.14
Maximum CCR 96.82 99.9 96.3
Automatic Weight CCR 96.35 99.87 95.37

11. 6 F F F Iris F F F 0.7
F F F 96.82 F F F F F 96.35
F F F F F F F 0.47 F F F
F Wine F F F 0.2 F F F 99.90 F F
F F F 99.87 F F F F
F F F 0.03 F F F F Zoo F F
F 0.1 F F F 96.30 F F F F
F 95.37 F F F F F F F 0.93
F F F F F ˈ 6
96.82
99.9
96.396.35
99.87
95.37
95
95.5
96
96.5
97
97.5
98
98.5
99
99.5
100
Iris Wine Zoo
Percent
Maximum CCR
Automatic CCR
6: F F F Manual Automatic
6 3 F F F F
F F F F F F
Class Zoo F F 1 F F

12. F F F F F
F F F F F F Class
F F F F
F F F F
F F +0.1 +1
ˈ F F F
F F
1. F F F F F
2. F F F F
+0.1 +1
3. F F 1 F F F
4. F F F F F
5. F F F F F
Class
F F F ˈ F F
F F
F F F ˈ F
F F F F ˈ
F F F +0.1 +1 F F F F F F F
F F
F UCI Machine Learning Repository F
F F F F
F
F F F F F F F
F F F F F F ˈ F F
F F F F F
F F F
F F (Prediction) (Pattern
Recognition) ˈ F

13. F
Berry, M.J.A., & Linoff, G. (1997). Data Mining Techniques. New York,
John Wiley & Sons.
Blum, A. (1992). Neural Networks in C++. New York, Wiley.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy
estimation and model selection. Proceedings of the Fourteenth International Joint
Conference on Artificial Intelligence 2 (12), 1137 1143.
Korenberg, M.J. (2007). Microarray Data Analysis: Methods and Applications.
Humana Press.
Kotsiantis, S. (2007). Supervised Machine Learning: A Review of Classification
Techniques. Informatica Journal 31, 249-268.
Mandal S. (2004). Artificial Neural Network and IT Application in Coastal Urban
Development. STTP conference, NITK, 180.