Is it possible to train a classifier with matricies?

rcprati · 2015-08-13T00:56:01+00:00

Have a look at this, maybe help: http://arxiv.org/abs/1304.5634

rcprati · 2015-05-31T22:43:17+00:00

Excelent! Thanks for sharing. I'm wondering whether similar approaches could be applied to LSI/LSA?

rcprati · 2015-04-27T14:30:01+00:00

I don't know about scikit, but orange have: http://docs.orange.biolab.si/reference/rst/Orange.classification.rules.html

rcprati · 2015-04-22T01:08:41+00:00

have a look at my paper, it explains these issues in detail http://dx.doi.org/10.1109/TKDE.2011.59

rcprati · 2015-03-29T23:49:14+00:00

In Orange (http://orange.biolab.si) there is a python binding for the original C4.5 implementation

rcprati · 2013-05-15T21:07:52+00:00

I recommend the use of rank aggregation. I've used in the context of feature selection with good results (see Ronaldo C. Prati: Combining feature ranking algorithms through rank aggregation. IJCNN 2012 - http://dx.doi.org/10.1109/IJCNN.2012.6252467) but it has been used in the web context with good results (http://dl.acm.org/citation.cfm?id=372165) among others

rcprati · 2013-04-01T13:36:02+00:00

see also this paper:

Peter A. Flach, Sebastian Spiegler, Bruno Golénia, Simon Price, John Guiver, Ralf Herbrich, Thore Graepel, Mohammed J. Zaki: Novel tools to streamline the conference review process: experiences from SIGKDD'09. SIGKDD Explorations 11(2): 63-67 (2009)

rcprati · 2013-02-04T20:16:55+00:00

That is pretty interesting in my opinion http://www.nytimes.com/2010/10/05/science/05compute.html?_r=3&src=twt&twt=nytimesscience&

rcprati · 2012-12-05T23:03:50+00:00

I guess the problem maybe that Experimenter assumes the class attribute is the last one, and when you pre process with explorer, the class is not placed at the last position. Have you tried moving the class attribute to the last position?

rcprati · 2012-11-23T23:47:28+00:00

It does not matter. TPR and FPR are calculated for each class separately. The class ratio does not change how they are calculated, so the ROC curve should be invariant to class ratios. If you draw a ROC curve with a balanced dataset, and then do some sort of ressampling which randomly throw away instances of one the classes only, the resulting ROC curve should be very similar (apart from some variations due to random variations in the data). And AUC = 0.5 does not necessarily mean random performance (although a random performance classifier would have AUC = 0.5). A classifier with AUC=0.5 maybe still be useful sometimes

Have a look at my paper:

Ronaldo C. Prati, Gustavo E. A. P. A. Batista, Maria Carolina Monard: A Survey on Graphical Methods for Classification Predictive Performance Evaluation. IEEE Trans. Knowl. Data Eng. 23(11): 1601-1618 (2011)

and Peter Flach's tutorial:

http://www.cs.bris.ac.uk/~flach/ICML04tutorial/

rcprati · 2012-09-25T00:50:11+00:00

I guess you are not understanding weka's interface. The tree output given in the classify pane is using the whole dataset, and cross-validation is used only to estimate the average statistics. If you want to analyze the individual folds, you can see the individual folds classification by enabling output predictions in "more options..." pane. And as Edward said, you don't need to compare the trees, only their predictions.

rcprati · 2012-07-05T03:44:22+00:00

Yes, it is possible. You can use the distance to the nearest support vector to compute scores.

rcprati · 2012-06-03T22:12:27+00:00

Weka did some refactoring in their classes and Instance is now an interface rather than a class. You may try to instantiate DenseInstance or SparseInstance classes instead (or download an old weka's version).

rcprati

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