Well, I think that classification could be viewed as a way of coding of information in sampled areas. In particular for soil properties continuos or fuzzy classification seems to work properly. Then, avoiding to talk about the non-convexity of kriging, we can interpolate before or after performing classification. But after all, also classification algorithms are a regression problem.......
Bye
Sebastiano
At 11.25 31/08/2005, Gregoire Dubois wrote:
I recently attended a presentation about the mapping of soil properties. Kriging was applied and I was wondering why a regression technique was used instead of a classification algorithm.
Delineating soil properties seemed to be, at first sight, a classification problem than a regression case. This was at first sight and we didn't debate much on this issue unfortunately.
Indicator kriging (IK) is somehow a bridge between these two issues (regression versus classification) and its simplicity in use and concept makes it very attractive to solve many problems.
Now I wonder (again) if there are some fundamental papers comparing IK to classification algorithms (e.g. Support Vector Machine, SVM). In the same way, SVM used for regression seems to be not that uncommon as well. So where is the borderline? When are we facing a classification problem and when is it a regression problem? I am not sure the borderline is always that obvious.
I am not answering Sebastiano's mail here but would be curious to see on this list a debate on "regression versus classification"... I presume there may there some material as well regarding the issue discussed below.
Best regards,
Gregoire
- -----Original Message-----
- From: seba [ mailto:[EMAIL PROTECTED]]
- Sent: 30 August 2005 18:17
- To: ai-geostats@unil.ch
- Subject: [ai-geostats] natural neighbor applied to indicator transforms
- Dear list members
- I would like to have some comments, suggestions or critics about the following topic:
- building a (preliminary) local uncertainty model of the spatial distribution of discrete (categorical) variables by means of natural neighbor interpolation method applied to indicator transforms.
- From my perspective, interpolating indicator variables (well, at the end an indicator variable is the probability of occurrence of a given class) by means of a method like natural neighbor is an easy and quick way to build a (preliminary) model of local uncertainty of the studied properties, avoiding problems of order relation violations.
- In my specific case I apply natural neighbor interpolation to indicator transforms representing lithological classes in the same way in which direct indicator kriging is applied. In this way, looking at the spatial distribution of the probability of occurrence of lithologies (or at the distribution of the lithological classes, if some classification algorithm is applied) I can have a first idea of the spatial distribution of lithologies. Clearly this method is utilized only as an explorative and preliminary data analysis tool.
- Thank you in advance for your replies.
- S. Trevisani
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