splice.arff

是UCI数据库中的一些有代表性的数据集

%       problem posed in this dataset is to recognize, given a sequence of DNA, the
%       boundaries between exons (the parts of the DNA sequence retained after
%       splicing) and introns (the parts of the DNA sequence that are spliced
%       out). This problem consists of two subtasks: recognizing exon/intron
%       boundaries (referred to as EI sites), and recognizing intron/exon boundaries
%       (IE sites). (In the biological community, IE borders are referred to
%       a ``acceptors'' while EI borders are referred to as ``donors''.)
% 
%    This dataset has been developed to help evaluate a "hybrid" learning
%    algorithm (KBANN) that uses examples to inductively refine preexisting
%    knowledge.  Using a "ten-fold cross-validation" methodology on 1000
%    examples randomly selected from the complete set of 3190, the following 
%    error rates were produced by various ML algorithms (all experiments
%    run at the Univ of Wisconsin, sometimes with local implementations
%    of published algorithms). 
% 
%                 System	       Neither    EI      IE
%                 ----------     -------  -----   -----
% 		KBANN    	 4.62    7.56    8.47
% 		BACKPROP    	 5.29    5.74   10.75
% 		PEBLS    	 6.86    8.18    7.55
% 		PERCEPTRON    	 3.99   16.32   17.41
% 		ID3    		 8.84   10.58   13.99
% 		COBWEB   	11.80   15.04    9.46
% 		Near. Neighbor	31.11   11.65    9.09