What is CLOP?
CLOP stands for Challenge Learning Object Package. It is an
object-oriented Matlab(R) Machine Learning package. CLOP is based on the
Spider developed
at the Max Planck Institute for Biological Cybernetics and integrates software
from several sources, see the credits. It was developed
to support challenges in Machine
Learning, and it includes the best working methods from these challenges,
in addition to the Spider library. You can also access from CLOP Weka and
R functions.
CLOP and the Spider are built on two simple abstractions: data and
algorithm. Once you load some data matrix X and target vector Y and
create a data object: > training_data = data(X,
Y);
You just need to instanciate an algorithm, say a Support Vector Classifier
(svc): > my_svc = svc;
Then you train it by calling the method train (in Matlab, functions,
which have an object as their first argument are methods of that object): [training_resu, trained_svc]
= train(my_svc, training_data);
The object resu contained the predictions on training data. The trained model
may then be tested with the method test on test_data test_resu = test(trained_svc,
test_data);
Compound models can be built by chaining algorithms, including preprocessing,
predictors, and postprocessing and/or building ensembles of models voting
towards the final decision. The resulting compound model is then trained
and tested by calling train and test; it knows how to train and test itself
by calling the train and test methods of its components.
Download CLOP
Before you download CLOP, please make sure you read the license agreement
and the disclaimer.
There are several versions of CLOP. We recommend you download the last
one:
Book on feature selection version: Featbook
version (April 2005)
Installation instructions
==> Windows users will just have to run a script to set the Matlab path
properly to use most functions.
==> Unix users will have to compile the LibSVM package if they want to
use support vector machines. Please use the latest
Makefile.
==> All users will have to install R to use random forests (RF and RFFS).
Make sure you remove and file named Clop/challenge_objects/packages/Rlink/__Rpath.
When you first start RF or RFFS, you will be prompted for the path of the
R executable.
Feature Extraction,
Foundations and Applications, Isabelle Guyon, Steve Gunn,
Masoud Nikravesh, and Lofti Zadeh, Editors.
Series Studies in Fuzziness and Soft Computing, Physica-Verlag, Springer,
2006.