Name: World development indicators: Life expectancy prediction dataset
Description: The World Development Indicators from the World Bank contain over a thousand annual indicators of economic development from hundreds of countries around the world.
License: The World Banl terms of use: http://web.worldbank.org/WBSITE/EXTERNAL/0,,contentMDK:22547097~pagePK:50016803~piPK:50016805~theSitePK:13,00.html
License: The World Banl terms of use: http://web.worldbank.org/WBSITE/EXTERNAL/0,,contentMDK:22547097~pagePK:50016803~piPK:50016805~theSitePK:13,00.html The World Bank Terms of Use
This dataset was collected for use within the DARPA Data Driven Discovery of Models (D3M) program.
ID: SEMI_1040_sylva_prior
Name: SEMI_1040_sylva_prior_dataset
Description: SYLVA is the ecology database
ID: SEMI_1040_sylva_prior_dataset
Name: SEMI 1040 sylva prior dataset
Description: SYLVA is the ecology database
The task of SYLVA is to classify forest cover types. The forest cover type for 30 x 30 meter cells is obtained from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. We brought it back to a two-class classification problem (classifying Ponderosa pine vs. everything else). The agnostic learning track data consists in 216 input variables. Each pattern is composed of 4 records: 2 true records matching the target and 2 records picked at random. Thus 1/2 of the features are distracters. The prior knowledge track data is identical to the agnostic learning track data, except that the distracters are removed and the identity of the features is revealed.
Citation: Datasets from the Agnostic Learning vs. Prior Knowledge Challenge (http://www.agnostic.inf.ethz.ch) Note: Derived from the covertype dataset Dataset from: http://www.agnostic.inf.ethz.ch/datasets.php Modified by TunedIT (converted to ARFF format)
This dataset was collected for use within the DARPA Data Driven Discovery of Models (D3M) program.
ID: SEMI_1044_eye_movements
Name: SEMI_1044_eye_movements_dataset
Description: Competition 1 (preprocessed data)
ID: SEMI_1044_eye_movements_dataset
Name: SEMI 1044 eye movements dataset
Description: Competition 1 (preprocessed data)
A straight-forward classification task. We provide pre-computed feature vectors for each word in the eye movement trajectory, with class labels.
The dataset consist of several assignments. Each assignment consists of a question followed by ten sentences (titles of news articles). One of the sentences is the correct answer to the question (C) and five of the sentences are irrelevant to the question (I). Four of the sentences are relevant to the question (R), but they do not answer it.
Citation: Jarkko Salojarvi, Kai Puolamaki, Jaana Simola, Lauri Kovanen, Ilpo Kojo, Samuel Kaski. Inferring Relevance from Eye Movements: Feature Extraction. Helsinki University of Technology, Publications in Computer and Information Science, Report A82. 3 March 2005. Data set at http://www.cis.hut.fi/eyechallenge2005/
Citation: Author: [Mike Chapman, Galaxy Global Corporation](Robert.Chapman@ivv.nasa.gov) Source: [PROMISE Repository](http://promise.site.uottawa.ca/SERepository) Please cite: please follow the acknowledgment guidelines posted on [the PROMISE repository web page](http://promise.site.uottawa.ca/SERepository). This is a PROMISE data set made publicly available in order to encourage repeatable, verifiable, refutable, and/or improvable predictive models of software engineering. If you publish material based on PROMISE data sets then, please follow the acknowledgment guidelines posted on [the PROMISE repository web page](http://promise.site.uottawa.ca/SERepository). ## Title/Topic JM1/software defect prediction ## Sources * Creators: NASA, then the NASA Metrics Data Program, http://mdp.ivv.nasa.gov. * Contacts: * Mike Chapman, Galaxy Global Corporation (Robert.Chapman@ivv.nasa.gov) +1-304-367-8341 * Pat Callis, NASA, NASA project manager for MDP (Patrick.E.Callis@ivv.nasa.gov) +1-304-367-8309 * Donor: Tim Menzies (tim@barmag.net)
This dataset was collected for use within the DARPA Data Driven Discovery of Models (D3M) program.
ID: SEMI_1459_artificial_characters
Name: SEMI_1459_artificial_characters_dataset
Description: This database has been artificially generated. It describes the structure of the capital letters A, C, D, E, F, G, H, L, P, R, indicated by a number 1-10, in that order (A=1,C=2,...). Each letter's structure is described by a set of segments (lines) which resemble the way an automatic program would segment an image. The dataset consists of 600 such descriptions per letter.
Description: This database has been artificially generated. It describes the structure of the capital letters A, C, D, E, F, G, H, L, P, R, indicated by a number 1-10, in that order (A=1,C=2,...). Each letter's structure is described by a set of segments (lines) which resemble the way an automatic program would segment an image. The dataset consists of 600 such descriptions per letter.
Citation: @data{DVN/29715_2015, author = {Goldsmith, Benjamin E and Butcher, Charles R and Semenovich, Dimitri and Sowmya, Arcot}, publisher = {Harvard Dataverse}, title = {{Replication data for: Forecasting the onset of genocide and politicide: Annual out-of-sample forecasts on a global dataset, 1988�2003}}, UNF = {UNF:5:fqBFZdPDGOQweuIffdQ2pQ==}, year = {2015}, version = {V1}, doi = {10.7910/DVN/29715}, url = {https://doi.org/10.7910/DVN/29715}}
Name: World development indicators: Life expectancy prediction dataset
Description: The World Development Indicators from the World Bank contain over a thousand annual indicators of economic development from hundreds of countries around the world.
License: The World Banl terms of use: http://web.worldbank.org/WBSITE/EXTERNAL/0,,contentMDK:22547097~pagePK:50016803~piPK:50016805~theSitePK:13,00.html
License: The World Banl terms of use: http://web.worldbank.org/WBSITE/EXTERNAL/0,,contentMDK:22547097~pagePK:50016803~piPK:50016805~theSitePK:13,00.html The World Bank Terms of Use
Citation: @misc{Dua:2017, author = {Dheeru, Dua and Karra Taniskidou, Efi}, year = {2017}, title = {{UCI} Machine Learning Repository}, url = {http://archive.ics.uci.edu/ml}, institution = {University of California, Irvine, School of Information and Computer Sciences} }
Citation: @misc{Dua:2017, author = {Dheeru, Dua and Karra Taniskidou, Efi}, year = {2017}, title = {{UCI} Machine Learning Repository}, url = {http://archive.ics.uci.edu/ml}, institution = {University of California, Irvine, School of Information and Computer Sciences} }
Citation: @inproceedings{cruz2015grouping, title={Grouping similar trajectories for carpooling purposes},author={Cruz, Michael O and Macedo, Hendrik and Guimaraes, Adolfo},booktitle={Intelligent Systems (BRACIS), 2015 Brazilian Conference on},pages={234--239}, year={2015},organization={IEEE}}
This dataset was collected for use within the DARPA Data Driven Discovery of Models (D3M) program.
ID: uu7_pima_diabetes
Name: uu7_pima_diabetes_dataset
Description: This is a a two-class classification problem to distinguish between absence and presence of diabetes.
ID: uu7_pima_diabetes_dataset
Name: Pima Diabetes Data Set
Description: This is a a two-class classification problem to distinguish between absence and presence of diabetes.
For LUPI processing, the features are split into two groups:
- standard features (columns 2-6, 8) are physically observable properties during a routine doctor visit
- privileged features (columns 1,7 ) are private information (number of pregnancies and diabetes pedigree function, which is the presence of diabetes among patient's relatives), which may not be available due to lack of recordkeeping.
License: CC-BY license
License Link: -NA-
Source: OpenML
Source Link: https://www.openml.org/d/37
Citation: @misc{Dua:2017, author = {Dheeru, Dua and Karra Taniskidou, Efi}, year = {2017}, title = {{UCI} Machine Learning Repository}, url = {http://archive.ics.uci.edu/ml}, institution = {University of California, Irvine, School of Information and Computer Sciences} }
Citation: @ieee{fei_fei_2004_383, author = {L. Fei-Fei and R. Fergus and P. Perona}, title = {{Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories}}, month = june}
Citation: @ieee{fei_fei_2004_383, author = {L. Fei-Fei and R. Fergus and P. Perona}, title = {{Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories}}, month = june} @article{fei2006one, title={One-shot learning of object categories}, author={Fei-Fei, Li and Fergus, Rob and Perona, Pietro}, journal={IEEE transactions on pattern analysis and machine intelligence}, volume={28}, number={4}, pages={594--611}, year={2006}, publisher={IEEE}}
