README.md file generated

README.md

@@ -1 +1,56 @@
-ExecuteMulan
+# Executing Multi-Label Learning methods from Mulan.
+
+[Mulan](http://mulan.sourceforge.net/) is an open-source Java library for learning from multi-label datasets; including both Multi-Label Classification (MLC) and Multi-Target Regression (MTR) methods. The Mulan library only provides a Java API, but no any GUI or command-line interface to execute its methods.
+
+In this repository, we created an executable .jar file to execute MLC and MTR methods from the Mulan library. The aim of this project was to have a personal way to execute experiments with the different methods as simple as possible.
+
+To execute it, the following command have to be executed in a console:
+```sh
+java -jar ExecuteMulan.jar [parameters]
+```
+
+The jar file allows several parameters to indicate the method to execute, the dataset, and so on. The different parameters are the following:
+* With the ```-t``` parameter, we define the .arff filename of the training dataset.
+* With the ```-T``` parameter, we define the .arff filename of the test dataset.
+* With the ```-x``` parameter, we define the .xml filename of the dataset [(know more about the format of the datasets)](http://www.uco.es/kdis/mllresources/).
+* With the ```-a``` parameter, the MLC or MTR method to be executed is indicated. Below is provided a list with all the available methods in this jar.
+* With the ```-o``` parameter, we define the filename of the putput file where the results are stored as a .csv file.
+* With the ```-i``` parameter, the number of different seeds for random numbers (for those algorithms that need) is indicated; i.e., the number of executions for those algorithms that use random numbers. If this parameter is passed to a method that does not use random numbers, it is omitted. By default, its value is 10.
+* The ```-o``` parameter, indicates if, for the macro-averaged measures, the values of the measure for each label is reported or not. It could take the values 0 (false) and 1 (true). By default, its value is 0.
+
+The full list of methods availables in this jar is provided below. It is shown the value of the ```-a``` parameter and the full name of the method:
+* Multi-label classification methods:
+  * ```AdaBoostMH```: AdaBoost.MH
+  * ```BPMLL```: Back-Propagation for MLL
+  * ```BR```: 
+  * ```CC```: 
+  * ```CDE```: 
+  * ```CLR```: 
+  * ```EBR```: 
+  * ```ECC```: 
+  * ```EPS```: 
+  * ```HOMER```: 
+  * ```IBLR```: 
+  * ```LP```: 
+  * ```LPBR```: 
+  * ```MLkNN```: 
+  * ```MLS```: 
+  * ```PS```: 
+  * ```RAkEL```: 
+  * ```RFPCT```: 
+* Multi-target regression methods:
+  * ```ERC```: 
+  * ```RC```: 
+  * ```RLC```: 
+  * ```ST```: 
+  * ```SST```: 
+
+
+
+### References
+
+<a name="Hal09"></a>**[Hal09]** M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. (2009). The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11(1), 10-18.
+
+<a name="Tso11"></a>**[Tso11]** G. Tsoumakas, E. Spyromitros-Xioufis, J. Vilcek, and I. Vlahavas. (2011). Mulan: A java library for multi-label learning. Journal of Machine Learning Research, 12, 2411-2414.
+
+<a name="Ven08"></a>**[Ven08]** S. Ventura, C. Romero, A. Zafra, J. A. Delgado, and C. Hervás. (2008). JCLEC: a Java framework for evolutionary computation. Soft Computing, 12(4), 381-392.

src/executeMulan/Main.java

@@ -39,7 +39,7 @@ public static void showUse()
 			//Regression algorithms
 		System.out.println("\t\tRegression algorithms:");
 		System.out.println("\t\t\tERC -> Ensemble of Regressor Chains");
-		//System.out.println("\t\t\tMORF -> Multi-Output Random Forest");
+//		System.out.println("\t\t\tMORF -> Multi-Output Random Forest");
 		System.out.println("\t\t\tRC -> Regressor Chains");
 		System.out.println("\t\t\tRLC -> Random Linear Combinations Normalized");
 		System.out.println("\t\t\tST -> Single Target");
@@ -236,7 +236,7 @@ else if(avalue.equalsIgnoreCase("SST"))
 		else
 		{
 			showUse();
-			System.out.println("The algorithm is not defined" + avalue);
+			System.out.println("The algorithm \'" + avalue + "\' is not defined");
 			System.exit(-1);
 		}
 	}