Introduction
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We maximize the structured and unstructured data classification accuracy by applying the classification and regression approach.
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We reduce the error rate using two-level visual information processing (2LVIP) techniques, which help to minimize the misclassification rate.
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Furthermore, we obtain the maximum information gain value for both the structured and unstructured data.
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Finally, we improve the precision by classifying the structured and unstructured data and reduce the error by combining the classification and regression methods.
Related works
Proposed 2LVIP method
Classification via the discrete finite value
Minimizing the errors with regression
Results and discussion
Precision analysis
Information gain analysis
Error analysis
Analysis of the processing time
Metrics | RF-KLT | MOD-3D LIDAR | UFO | 2LVIP |
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Precision | 0.8571 | 0.873 | 0.8947 | 0.911 |
Gain % | 80.7 | 89.1 | 95.43 | 97.83 |
Error | 0.0644 | 0.593 | 0.0461 | 0.0393 |
Processing time (ms) | 624.97 | 593.45 | 372.42 | 414.94 |
Metrics | RF-KLT | MOD-3D LIDAR | UFO | 2LVIP |
---|---|---|---|---|
Precision | 0.871 | 0.8847 | 0.9161 | 0.936 |
Error | 0.0734 | 0.0558 | 0.0452 | 0.0325 |
Processing time (ms) | 642.55 | 591.2 | 496.8 | 424.11 |