*******************
***  R rmcfs    ***
*******************
1.1.2
    	MCFS-ID works on numeric target (new classifier M5 (regression tree) is implemented in MCFS-ID)
    	fix.data replaces value '?' by NA
    	fix in write.arff
    	dmLab.jar version 2.1.2	    
1.1.1
		function info() is now showme()
		minor fixes
1.1.0
		first official CRAN release
		functionality of the package is highly simplified - available 12 basic functions
		new names of functions: save.result()/read.result() are now export.result()/import.result(); build.ID.graph() is now build.idgraph()
		removed useles prefix 'mcfs.' from input params in function mcfs()
		function fix.data() combines all functionality of: fix.data.values(), fix.data.names() and fix.data.types()
		function mcfc() returns 'mcfs' object - one plot() function (parameter 'type') and one print() function for 'mcfs' object
		function build.idgraph() returns 'idgraph' object - new plot() function for 'idgraph' object
		function plot(type="ri") implements plot.permutations functionality - now it shows maxRI values(if 'plot_permutations' = TRUE)
		fixed margins in plot.idgraph() - now idgraph uses entire plot space
		removed curved_edges param from plot.idgraph() - curved_edges are always on
		plot.idgraph() has new parameter label.dist() that defines distance of labels to corresponding nodes
		build.idgraph() implements get.min.ID() functionality - get.min.ID() is not visible
		new function 'artificial.data(1000)' that creates artificial example
		many minor fixes to meet CRAN rules
		RD files updated by artificial.data example
		function mcfc() has new seed parameter - now it is possible to replicate the result
		dmLab.jar version 2.1.0
1.0.6
  		function write.adx is reiplemented and now it uses a smart exporting (chunk based) for huge datasets
  		function write.arff is reiplemented and now it uses a smart exporting (chunk based) for huge datasets
		function info() extended and changed - better for huge data.frames
  		function fix.data.names added (colNames are cleaned from various unwanted chars e.g. "|", "#", ",")
  		fix.data.types and fix.data.values works much faster on huge data
  		fixed plot.distances (x axis shows correct values projections(s))
  		dmLab.jar version 2.0.6  		      
1.0.5
    	useless parameter 'iType' is removed from plot.ID.graph function
    	in mcfs function removed parameter 'splitSetSizeLimit' splitSetSize does the job
    	new parameter in mcfs function 'cutoffMethod'
    	fix of helps (mcfs, build.ID.graph, plot.ID.graph)
    	running mcfs on one class data is not allowed
    	function 'model.frame' replaced by faster and more stable own implementation
    	updated and improved help *.rd files
		dmLab.jar version 2.0.5
1.0.4
    	new parameter in plot.permutations (parameter 'type')
    	cleaning of temporary files after reading result by mcfs function
    	dmLab.jar version 2.0.4
1.0.3
    	parameters u & v are available in mcfs function
1.0.2
		new parameter in plot.ID.graph (curved.edges=T)
	  	refactoring: importances -> RI, interactions -> ID [plot.ID.graph, build.ID.graph, plot.RI, plot.ID]
1.0.1
	  	new function build.rules
	  	updated help *.rd files (in mcfs introduced parameters s,t according to official papers about MCFS-ID)	
1.0.0
		first version of rmcfs


***********************
***   Java dmLab    ***
***********************
2.1.2
		regression tree M5 added - MCFS-ID works on numeric target
		in the case of regression task the following quality measures are calculated: "pearson","MAE","RMSE","SMAPE" (cmatrix is not calculated)
		distance is calculated on minimum 30 top attributes - better presentation of progress for small number of columns	
		adx file does not require "decision(all)" use "decision" instead (backward compatible)
2.1.1
    	minor fixes
2.1.0
		huge source code refactoring and cleaning
		ADXClassifier works on nominal and numeric data (discretization is built-in now)
		removed useless 'discretizeData' parameter that control discretization in MCFS (never used)
		cleaning in Params and in all inherit classes 
		cleaning in Classification & removed useless discretization from there
		removed useless parameters 'split', 'project', 'testFileName'
		fixed AttributesRI fixed measures projecions, freq (and changed its name to classifiers) now _RI.csv file is more readable  		
		removed cloudgarden Layouts
		new seed parameter - it helps to repeat MCFS-ID results
		random reordering of input columns before the split - decision tree bias removed
2.0.6
		huge source code refactoring and cleaning
		reimplementation of base Array classes and functions (Container replaced by Array, FArray, SArray)
		cleaning in classes Attribute, Domain (ADXDomain, SDomain, FDomain), Discretization
		cleaning in DiscFunctions, ExtFunctions, SelectFunctions that operate on Array/FArray/SArray
		Array/FArray/SArray need less memory				
2.0.5		
		parameter splitSetSizeLimit removed
		parameter balanceClasses removed, balancing is turned off as default
		fix in print information
		parameter mcfs.topRankingMethod replaced by mcfs.cutoffMethod 
2.0.4
		shapiro replaced by anderson darling
		during permutations experiment normality of each attribute (pval) is calc as well as student_t (pval)
		refactoring of parameters mcfs.progressShow and mcfs.progressInterval
		added distlib-0.4.1-bin.jar
		fix in main panel set inputFiles as well as inputFileName		 
2.0.3
		final rules are created on full data but there is cross validation result added instead as re classification of training dataset
		confMatrix printing change
		rename of result files: importances -> ri, connections -> id 
2.0.2
		all headers updated in source code
		fix in finding number of attributes to feed CV experiment (now getCutoffValues always returns 5 values) 
2.0.1
		mcfs works on multiple input files it runs experiments one by one on the same parameters
2.0.0
		final cv is based on all determined cutoff values. Additionally [0.25,0.5,0.75] * min(cutoff) and [1.25,1.5,2]*max(cutoff)
		JRIP added to CV
		JRIP rules created on topRankingMethod(cutoff) attributes and entire input dataset
		attribute name in ADX can contain any character but must be quoted  
		minor improvements and fixes
1.9.9
		Stability improvements
		WekaClassifier in memory mode is default - speed up for about 30% for some data and configurations
		New rewritten AttributesConnection		
		1 result from all cut off methods
		Contrast Attributes as permutation of originals 
		parameter topRankingSize=projectionSize
		final CV experiment is added. Now you may test classification accuracy based on top ranking features. Classifiers: c4.5, nb, svm, knn, logistic regression.
				
		cutpoint -> cutoff experiment now it is integrated with MCFS. If cutoffPermutations=0 then 
			classic MCFS without permutation will be run.
		plenty minor changes
		refactoring
		graph viewer upgraded to v 2.0 now it presents directed graphs
1.9.4
		new cutoff methods
1.9.3
		fix in matrix saving
1.9.2
		refactoring and source code cleaning
		confusion matrix added
1.9.1
		ID grapph ignores contrast features
1.9.0
		parameter useInfoGain=false replaced by useGainRatio=true
		new method addConnectionsDirected()
		parameter maxConnectionLevel replaced by maxConnectionDepth
		
************************
***  Known Problems  ***
************************
		contrastAttributes functionality is not fully implemented and tested (keep mcfs.contrastAttr = false)		  	
  	XML import and export in graphViewer does not work correctly
  	ADXClassifier does not perform very well as it used years ago it needs review
