Posted by
marcelo_chong on
Aug 23, 2014; 3:33pm
URL: http://imagej.273.s1.nabble.com/Creating-DataSet-after-building-classification-with-Trainable-Weka-Segmentation-tp5009283p5009315.html
Hello Ignacio,
Thanks for the reply again !
1. Solved, thanks a lot.
2. Sorry for my poor understanding. I still cannot success.
As I follow your steps:
------------------------------------------------------------------------
1. save the information from the 15 images you loaded in the plugin as
data-train.arff
DONE !
I got 4867 instances with 119 attributes in the arff file.
2. exit the plugin
DONE !
3. open the test images as a stack and call the plugin
DONE !
4. trace some pixels for all classes you have (you must have exactly the
same classes as before)
As I have to extract the same features. So i need to LOAD the data-train.arff again, right?
I load the data-train.arff, then I start to trace some pixels and in each images and classed it as A or B after the trace.
5. "save data" as "data-test.arff"
After trace all 10 images and make classification with the trace, I press save data and save as data-test.arff
Then the log is :
Creating feature stack...
Updating features of slice number 1...
Updating features of slice number 2...
Updating features of slice number 3...
Updating features of slice number 4...
Updating features of slice number 5...
Updating features of slice number 6...
Updating features of slice number 7...
Updating features of slice number 8...
Updating features of slice number 9...
Updating features of slice number 10...
Feature stack is now updated.
Training input:
# of pixels selected as class 1: 1772
# of pixels selected as class 2: 593
Merging data...
Finished: total number of instances = 7232
Writing training data: 7232 instances...
It created 7232 instance .....what i expect is 10 instance with 119 attributes.
6. click on the Weka button
Anyway, I still continue and try. I click on the weka button to load weka.
7. launch the Weka explorer
launch the explorer.
8. in the explorer, open the training arff file
open my data-trian.arff
9. click on classify and choose the classifier you want (the equivalent to
the plugin is a hr.irb.fastRandomForest.FastRandomForest with numTrees =
200 trees and numFeatures = 2).
done.
10. select as test the data-test.arff you saved and run the classification
the "Supplied test set", i choose my data-test.arff, and the outcome is :
=== Run information ===
Scheme: weka.classifiers.trees.RandomForest -I 200 -K 2 -S 1 -num-slots 1
Relation: segment
Instances: 4867
Attributes: 119
[list of attributes omitted]
Test mode: user supplied test set: size unknown (reading incrementally)
=== Classifier model (full training set) ===
Random forest of 200 trees, each constructed while considering 2 random features.
Out of bag error: 0.0643
Time taken to build model: 4.98 seconds
=== Evaluation on test set ===
=== Summary ===
Correctly Classified Instances 6707 92.7406 %
Incorrectly Classified Instances 525 7.2594 %
Kappa statistic 0.8363
Mean absolute error 0.1242
Root mean squared error 0.2294
Relative absolute error 26.4056 %
Root relative squared error 47.8019 %
Coverage of cases (0.95 level) 99.8202 %
Mean rel. region size (0.95 level) 73.4098 %
Total Number of Instances 7232
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure ROC Area PRC Area Class
0.976 0.162 0.918 0.976 0.946 0.978 0.986 class 1
0.838 0.024 0.949 0.838 0.89 0.978 0.967 class 2
Weighted Avg. 0.927 0.114 0.929 0.927 0.926 0.978 0.98
=== Confusion Matrix ===
a b <-- classified as
4579 114 | a = class 1
411 2128 | b = class 2
The question is, is the above step correct ? Coz i want to have 10 instances to be test to see rather it is class 1 or class 2, but now I don't know how to interpret the result.
Or I did wrong with some steps?
Thanks.
Regards,
Marcelo
Ignacio Arganda-Carreras wrote
Hello again, Marcelo!
> 1. For the stack image sequence, it works. Just one question abt it, do i
> have to load in all the same dimension images? Coz some images are not in
> the
> same size.
>
>
Unfortunately the plugin only takes stacks, so all the images should be of
the same size.
A solution would be to
1. load one image and call the plugin
2. do the training and save the traces (as ARFF)
3. exit the plugin
4. load the second image and call the plugin
5. load the ARFF file you just save
6. train with the new traces
The old traces will be taken into account as soon as you load the ARFF file.
>
> 2. This one I don't readily get it. First I train 15 images and it save to
> data-train.arff,. I check the arff file and it got 119 attributes with 4867
> instances, which the last attribute is the classification. Then now, I got
> another 10 images, and I want to know if i use the previous training data
> to
> build up a Tree or whatever, will it correctly classification this 10
> images. So first, I want to extract the 118 attributes(-1 for the
> classification attribute) from the 10 images. I open the 10 images in Fiji,
> and open the TWS, in the TWS, I "Load Data" and open the data-train.arff.
> Then, what shall I do to turn the 10 images into 10 instances with 118
> attribute ARFF file ?
>
You almost got it! This is what you have to do:
1. save the information from the 15 images you loaded in the plugin as
data-train.arff
2. exit the plugin
3. open the test images as a stack and call the plugin
4. trace some pixels for all classes you have (you must have exactly the
same classes as before)
5. "save data" as "data-test.arff"
6. click on the Weka button
7. launch the Weka explorer
8. in the explorer, open the training arff file
9. click on classify and choose the classifier you want (the equivalent to
the plugin is a hr.irb.fastRandomForest.FastRandomForest with numTrees =
200 trees and numFeatures = 2).
10. select as test the data-test.arff you saved and run the classification
ignacio
>
>
> thanks.
>
> Regards,
> Marcelo
>
>
>
>
>
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>
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--
Ignacio Arganda-Carreras, Ph.D.
Seung's lab, 46-5065
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
43 Vassar St.
Cambridge, MA 02139
USA
Phone: (001) 617-324-3747
Website:
http://bioweb.cnb.csic.es/~iarganda/index_EN.html--
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