i want to remove bad sernsing data from all these data. i want to choose an outlier algorithm
Try support vector machine: http://svmlight.joachims.org/
This one is from Matlab help (Machine Learning->Supervised Learning->SVM->Functions->fitcsvm->Examples):
Detect Outliers Using SVM and One-Class Learning
Load Fisher's iris data set. Remove the petal lengths and widths. Treat all irises as coming from the same class.
load fisheriris
X = meas(:,1:2);
y = ones(size(X,1),1);
.... and there are two more steps you need to do.
There are several methods available. if your data univariate you can use the given following
1. Grubbs test
2. Z Scores or Modified Z-Scores
3. Median Absolute Deviation (MADe)
4. Hample Method
6. Generalised ESD
7. Tukey Method
8. Boxplot
9. Quartile Methods
10.Dixon’s Test (The test has some
limitations to n
- Badges
- Science topic
- Similar topics
- Mathematics
- Algebra
- Matrix