Temporal Classification:Extending the Classification Paradigm to Multivariate Time Series

This paper is basically a part of the much detailed thesis dealing with the Australian Sign Language. They are using two gloves to capture the data and analyze the recognition rate. One of the gloves used is the Nintendo glove and the other is a device called the Flock data. Nintendo is a low cost glove with the cheap sensors and Flock is the complicated superior device. The data obtained is used in their classifier called the Tclass, which looks like a decision Tree type classifier, and using the different parameters of the Tclass results were obtained.

Since the data obtained from the Nintendo glove is noisy they have used smoothening to tune in their results which helped to get better accuracy. In the end they used a voting methodology to get the best learners, similar to the ada boost, to improve the accuracy and decrease the error.

With the flock, they did the same thing how ever they found that the smoothening is actually affecting the recognition results as the sensor data is already much refined with almost nil noise.

Discussion:

The have shown that their classifier called the Tclass was able to provide a low error rate, by using the ensemble. They tested their data on Nintendo and Flock and smoothening worked with the Nintendo and not with the flock. I believe,it is because the data from Nintendo is so noisy that the distinguishing features are suppressed by the noise, while in case of the flock data, if we tend to smoothen the refined data accuracy will drop as the distinguishing features associated with the data are smoothened. It would have been nice to read in actual what Tclass is and what it does.I believe the only good thing in the paper was the Tclass classifier which is actually some kind of decision tree based classifier which needs to be investigated.