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Data Set Analysis
- 1. Data Set Analysis Wall-Following Robot Navigation Data Data Set(4 US Sensor data) Wall-Following Robot Navigation Data Data Set(24 US Sensor data) Amit Ghosh 011132134
- 2. SCITOS G5 Ultrasonic Sensor MetraLabs develops professional mobile robot
- 4. Dataset Information (Source : UC Irvine Machine Learning Repository) Type Classification Characteristics Multivariate Number of Instance 5456 Number of Attributes 5 Attribute Characteristics real Date Donated 2010-08-04 Distinct Class 4 Missing Values? N/A
- 5. KNN K=5% K=10% K=15% K=20% 79.1239 % 75.8798 % 71.4626 % 67.6136 % *10-fold cross-validation
- 6. OneR (4611/5456 instances correct) *10 fold cross validation Accuracy 89.96 %
- 7. Naive Bayes Accuracy 89.11 % a b c d <-- classified as 1883 176 106 40 | a = Move-Forward 36 753 37 0 | b = Slight-Right-Turn 104 48 1927 18 | c = Sharp-Right-Turn 5 0 24 299 | d = Slight-Left-Turn *10-fold cross-validation Confusion Matrix
- 8. Decision Tree CART and Random Forest give same accuracy *10-fold cross-validation ID3 100 %
- 9. Applying this algorithm in 24 US Sensor model *10-fold cross-validation KNN NaiveBayes OneR ID3 88.1782 % 52.456 % 75.6415 % 99.6518 %
- 10. I select ID3 ● Highest Accuracy ● Easy to implement in Robot ● Only if else condition ● We can remove unnecessary sensor like US3 and US4 ● Reduce cost ● Reduce computational time ID3 100 %
- 11. Implementation if US1<=0.9 Sharp-Right-Turn else if US2<=0.494 Slight-Right-Turn else if US2<=0.901 Move-Forward else if US2>0.901 Slight-Left-Turn Collect sensor data from Robot Apply ID3 and Generate Rules/Condition Put the condition in the controller program
- 12. Actual Robot (SCITOS G5)
- 13. We can use this technique in with any kind of sensor based robot.