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Visits since 05/11/2005:
394485 
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 | Regression data sets |
The data sets shown in this section represents regression problems. Consequently, the output value of these data sets is real valued. Many machine learning approaches can be employed to deal with these problems.
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
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 in regression section of the repository. |
Below you can find all the Regression 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 the data set already partitioned, by means of a 5-folds cross validation procedure. These 5-fold partitions have been used in many works of the literature. See, for example:
M.J. Gacto, M. Galende, R. Alcalá, F. Herrera, METSK-HDe: A Multiobjective Evolutionary Algorithm to learn accurate TSK-fuzzy Systems in High-Dimensional and Large-Scale Regression Problems. Information Sciences 276 (2014) 63-79.
R. Alcalá, M.J. Gacto, F. Herrera, A Fast and Scalable Multi-Objective Genetic Fuzzy System for Linguistic Fuzzy Modeling in High-Dimensional Regression Problems. IEEE Transactions on Fuzzy Systems 19:4 (2011) 666-681.
M Antonelli, P Ducange, F Marcelloni, An efficient multi-objective evolutionary fuzzy system for regression problems, International Journal of Approximate Reasoning 54:9 (2013) 1434-1451.
Finally, we provide a header file 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. | Data set | 5-fcv | Header |
|---|---|---|---|---|---|---|
| diabetes | 2 (2/0/0) | 43 | No |  | |  |
| ele-1 | 2 (1/1/0) | 495 | No | | | |
| plastic | 2 (2/0/0) | 1650 | No | | | |
| quake | 3 (2/1/0) | 2178 | No | | | |
| laser | 4 (4/0/0) | 993 | No | | | |
| ele-2 | 4 (4/0/0) | 1056 | No | | | |
| autoMPG6 | 5 (2/3/0) | 392 | No | | | |
| friedman | 5 (5/0/0) | 1200 | No | | | |
| delta_ail | 5 (5/0/0) | 7129 | No | | | |
| machineCPU | 6 (0/6/0) | 209 | No | | | |
| dee | 6 (6/0/0) | 365 | No | | | |
| delta_elv | 6 (5/1/0) | 9517 | No | | | |
| autoMPG8 | 7 (2/5/0) | 392 | No | | | |
| ANACALT | 7 (7/0/0) | 4052 | No | | | |
| concrete | 8 (7/1/0) | 1030 | No | | | |
| abalone | 8 (7/1/0) | 4177 | No | | | |
| california | 8 (3/5/0) | 20640 | No | | | |
| stock | 9 (9/0/0) | 950 | No | | | |
| wizmir | 9 (9/0/0) | 1461 | No | | | |
| wankara | 9 (9/0/0) | 1609 | No | | | |
| mv | 10 (7/3/0) | 40768 | No | | | |
| forestFires | 12 (7/5/0) | 517 | No | | | |
| treasury | 15 (15/0/0) | 1049 | No | | | |
| mortgage | 15 (15/0/0) | 1049 | No | | | |
| baseball | 16 (2/14/0) | 337 | No | | | |
| house | 16 (10/6/0) | 22784 | No | | | |
| elevators | 18 (14/4/0) | 16599 | No | | | |
| compactiv | 21 (21/0/0) | 8192 | No | | | |
| pole | 26 (26/0/0) | 14998 | No | | | |
| puma32h | 32 (32/0/0) | 8192 | No | | | |
| ailerons | 40 (36/4/0) | 13750 | No | | | |
| tic | 85 (0/85/0) | 9822 | No | | | |
| All 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.
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.
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