KEEL: A software tool to assess evolutionary algorithms for Data Mining problems (regression, classification, clustering, pattern mining and so on)

Low quality data sets contains a mixture of crisp and fuzzy values. Concretely, each attribute can be continuous or discrete, and each one can be expressed as a crisp or as a fuzzy interval.

This format of representation allows researchers to define new kinds of challenging problems, where approaches capable of handle fuzzy-values attributes can obtain suitable results.

Each data file has the following structure:

@relation: Name of the data set
@attribute: Description of an attribute (one for each attribute)
@inputs: List with the names of the input attributes
@output: Name of the output attribute
@data: Starting tag of the data

In a low quality data set, each attribute, nominal or continuous can be expresed as a crisp interval ({red, green}, [MIN, MAX]) or as a fuzzy interval ({red|0.5, green|0.9}, [MIN,MOD,MAX] (MOD is the mode of the interval)).

The rest of the file contains all the examples belonging to the data set, expressed in comma sepparated values format.

We offer information about experimental studies using these data sets (result files, papers and more) in the Experimental studies with low quality data section of the repository.

Below you can find all the Low quality data sets available. For each data set, it is shown its name and its number of examples (instances) and attributes (the table details the number of Real/Integer/Nominal attributes in the data). In addition, the table shows if the corresponding data set has missing values or not.

The table allows to download each data set in KEEL format (inside a ZIP file). Additionally, it is possible to obtain for some data sets the partitions using a 10-folds validation procedure. These partitions and datasets were used in the following papers:

Palacios, A. M., Sánchez, L., Couso, I. Extending a simple genetic cooperative-competitive learning fuzzy classifier to low quality datasets. Evolutionary Intelligence 2:1-2 (2009) 73-84.

Palacios, A. M., Sánchez, L., Couso, I. Diagnosis of dyslexia with low quality data with genetic fuzzy systems. International Journal of Approximate Reasoning 51 (2010) 993-1009.

Finally, we provide a header file (in KEEL format) to give additional information about each data set and its attributes.

By clicking in the column headers, you can order the table by names (alphabetically), by the number of examples or attributes, or by the presence of missing values. Clicking again will sort the rows in reverse order.

Name	#Attributes (R/I/N)	#Examples	Miss Val.
long-4	4 (4/0/0)	25	No
100mlI-4	4 (4/0/0)	52	No
100mlP-4	4 (4/0/0)	52	No
dyslexic_12_4	12 (12/0/0)	65	Yes
Inexpert-57	52 (0/0/0)	54	Yes
All data sets

Collecting Data Sets

If you have some example data sets and you would like to share them with the rest of the research community by means of this page, please be so kind as to send your data to the Webmaster Team with the following information:

People answerable for the data (full name, affiliation, e-mail, web page, ...).
training and test data sets considered, preferably in ASCII format.
A brief description of the application.
References where it is used.
Results obtained by the methods proposed by the authors or used for comparison.
Type of experiment developed.
Any additional useful information.

Collecting Results

If you have applied your methods to some of the problems presented here we will be glad of showing your results in this page. Please be so kind as to send the following information to Webmaster Team:

Name of the application considered and type of experiment developed.
Results obtained by the methods proposed by the authors or used for comparison.
References where the results are shown.
Any additional useful information.

If you are interested on being informed of each update made in this page or you would like to comment on it, please contact with the Webmaster Team.