Short-Term Prediction of Sintering State Based on Improved Random Forest

Table 2. Accuracy of K-means
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Table 2. Accuracy of K-means
Geometric feature Area Major Minor Eccentricity Orientation
Accuracy 0.49 0.42 0.64 0.47 0.59
Geometric feature Equiv diameter Solidity Extent Perimeter Thinness ratio
Accuracy 0.51 0.35 0.51 0.47 0.37

Table 3. Accuracy of FCM
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Table 3. Accuracy of FCM
Geometric feature Area Major Minor Eccentricity Orientation
Accuracy 0.51 0.43 0.64 0.5 0.64
Geometric feature Equiv diameter Solidity Extent Perimeter Thinness ratio
Accuracy 0.51 0.48 0.39 0.47 0.46

Table 4. K-means geometric feature probability
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Table 4. K-means geometric feature probability
Geometric feature Area Major Minor Eccentricity Orientation
Probability 0.1 0.09 0.13 0.1 0.12
Geometric feature Equiv diameter Solidity Extent Perimeter Thinness ratio
Probability 0.11 0.07 0.11 0.09 0.08

Table 5. FCM geometric feature probability
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Table 5. FCM geometric feature probability
Geometric feature Area Major Minor Eccentricity Orientation
Probability 0.1 0.08 0.13 0.1 0.13
Geometric feature Equiv diameter Solidity Extent Perimeter Thinness ratio
Probability 0.1 0.1 0.08 0.09 0.09

Table 6. Accuracy of improved random forest algorithm for three categories of images
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Table 6. Accuracy of improved random forest algorithm for three categories of images
Method Normal burning Underburning Overheating
method
Conventional
85.71 91.67 91.67
SVM（RBF kernel function） 95.24 91.67 91.67
Improvement（K-means） 97.62 95.83 100
Improvement（FCM） 97.56 100 95.83

Table 7. Recall of improved random forest algorithm for three categories of images
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Table 7. Recall of improved random forest algorithm for three categories of images
Method Normal burning Underburning Overheating
Conventional method 94.74 81.48 88
SVM（RBF kernel function） 97.56 88 91.67
Improvement（K-means） 97.62 95.83 100
Improvement（FCM） 97.56 92.31 100

Table 8. Comparison of overall result

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Table 8. Comparison of overall result

Method	Accuracy /%	Precision /%	Recall /%	F1 value /%
Conventional method	88.89	89.68	88.07	88.87
SVM（RBF kernel function）	93.3333	92.86	92.41	92.63
Improvement（K-means）	97.78	97.82	97.82	97.82
Improvement（FCM）	96.67	97.80	96.62	97.21

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Fubin Wang, Rui Wang, Chen Wu. Short-Term Prediction of Sintering State Based on Improved Random Forest[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815018

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