{ "data_id": "58", "name": "vowel", "exact_name": "vowel", "version": 1, "version_label": "1", "description": "**Author**: \n**Source**: Unknown - \n**Please cite**: \n\n*** Please use version 2 of this dataset. This version has a train\/test feature that should be ignored in OpenML.\n\nIntroduction\n ============\n \n In my work on context-sensitive learning, I used the \"Deterding Vowel\n Recognition Data\", but I found it necessary to reformulate the data.\n Implicit in the original data is contextual information on the\n speaker's gender and identity. For my work, it was necessary to make\n this information explicit. The file \"vowel-context.data\" adds the\n speaker's sex and identity as new features. The format of the data file\n is described below.\n \n \n Peter Turney\n peter@ai.iit.nrc.ca\n \n \n \n References\n ==========\n \n P. Turney. \"Robust Classification With Context-Sensitive Features.\"\n Proceedings of the Sixth International Conference on Industrial\n and Engineering Applications of Artificial Intelligence and Expert\n Systems (IEA\/AIE-93): 268-276. 1993.\n \n URL: ftp:\/\/ai.iit.nrc.ca\/pub\/ksl-papers\/NRC-35074.ps.Z\n \n \n P. Turney. \"Exploiting Context When Learning to Classify.\"\n Proceedings of the European Conference on Machine Learning\n (ECML-93): 402-407. 1993.\n \n URL: ftp:\/\/ai.iit.nrc.ca\/pub\/ksl-papers\/NRC-35058.ps.Z\n \n \n \n File Structure\n ==============\n \n \n Column Description\n -------------------------------\n 0 Train or Test\n 1 Speaker Number\n 2 Sex\n 3 Feature 0\n 4 Feature 1\n 5 Feature 2\n 6 Feature 3\n 7 Feature 4\n 8 Feature 5\n 9 Feature 6\n 10 Feature 7\n 11 Feature 8\n 12 Feature 9\n 13 Class\n \n \n \n \n Numerical Codes\n ===============\n \n \n Speaker Code Number\n ---------------------------\n Andrew 0\n Bill 1\n David 2\n Mark 3\n Jo 4\n Kate 5\n Penny 6\n Rose 7\n Mike 8\n Nick 9\n Rich 10\n Tim 11\n Sarah 12\n Sue 13\n Wendy 14\n \n \n \n Set Number\n ---------------------------\n Train 0\n Test 1\n \n \n \n Sex Number\n ---------------------------\n Male 0\n Female 1\n \n \n \n Class Number\n ---------------------------\n hid 0\n hId 1\n hEd 2\n hAd 3\n hYd 4\n had 5\n hOd 6\n hod 7\n hUd 8\n hud 9\n hed 10\n \n \n \n \n \n Speaker Code Number Sex Train\/Test\n ---------------------------------------------------------------\n Andrew 0 0 0\n Bill 1 0 0\n David 2 0 0\n Mark 3 0 0\n Jo 4 1 0\n Kate 5 1 0\n Penny 6 1 0\n Rose 7 1 0\n Mike 8 0 1\n Nick 9 0 1\n Rich 10 0 1\n Tim 11 0 1\n Sarah 12 1 1\n Sue 13 1 1\n Wendy 14 1 1\n \n \n Num Instances: 990\n Num Attributes: 14\n Num missing: 0 \/ 0.0%\n\n name type enum ints real missing distinct (1)\n 1 'Train or Test' Enum 100% 0% 0% 0 \/ 0% 2 \/ 0% 0% \n 2 'Speaker Number' Enum 0% 100% 0% 0 \/ 0% 15 \/ 2% 0% \n 3 'Sex' Enum 0% 100% 0% 0 \/ 0% 2 \/ 0% 0% \n 4 'Feature 0' Real 0% 0% 100% 0 \/ 0% 853 \/ 86% 74% \n 5 'Feature 1' Real 0% 0% 100% 0 \/ 0% 877 \/ 89% 78% \n 6 'Feature 2' Real 0% 0% 100% 0 \/ 0% 815 \/ 82% 67% \n 7 'Feature 3' Real 0% 0% 100% 0 \/ 0% 836 \/ 84% 71% \n 8 'Feature 4' Real 0% 0% 100% 0 \/ 0% 803 \/ 81% 66% \n 9 'Feature 5' Real 0% 0% 100% 0 \/ 0% 798 \/ 81% 64% \n 10 'Feature 6' Real 0% 0% 100% 0 \/ 0% 748 \/ 76% 57% \n 11 'Feature 7' Real 0% 0% 100% 0 \/ 0% 794 \/ 80% 64% \n 12 'Feature 8' Real 0% 0% 100% 0 \/ 0% 788 \/ 80% 63% \n 13 'Feature 9' Real 0% 0% 100% 0 \/ 0% 775 \/ 78% 60% \n 14 'Class' Enum 0% 100% 0% 0 \/ 0% 11 \/ 1% 0% \n\n\n\n\n Relabeled values in attribute 'Speaker Number'\n From: 0 To: Andrew \n From: 1 To: Bill \n From: 2 To: David \n From: 3 To: Mark \n From: 4 To: Jo \n From: 5 To: Kate \n From: 6 To: Penny \n From: 7 To: Rose \n From: 8 To: Mike \n From: 9 To: Nick \n From: 10 To: Rich \n From: 11 To: Tim \n From: 12 To: Sarah \n From: 13 To: Sue \n From: 14 To: Wendy \n\n\n Relabeled values in attribute 'Sex'\n From: 0 To: Male \n From: 1 To: Female \n\n\n Relabeled values in attribute 'Class'\n From: 0 To: hid \n From: 1 To: hId \n From: 2 To: hEd \n From: 3 To: hAd \n From: 4 To: hYd \n From: 5 To: had \n From: 6 To: hOd \n From: 7 To: hod \n From: 8 To: hUd \n From: 9 To: hud \n From: 10 To: hed", "format": "ARFF", "uploader": "Jan van Rijn", "uploader_id": 1, "visibility": "public", "creator": null, "contributor": null, "date": "2014-04-06 23:23:26", "update_comment": "dataset should not be used (has train\/test split feature) and is set to private for now", "last_update": "2014-09-19 17:45:05", "licence": "Public", "status": "deactivated", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/58\/dataset_58_vowel.arff", "kaggle_url": null, "default_target_attribute": "Class", "row_id_attribute": null, "ignore_attribute": null, "runs": 458, "suggest": { "input": [ "vowel", "Introduction ============ In my work on context-sensitive learning, I used the \"Deterding Vowel Recognition Data\", but I found it necessary to reformulate the data. Implicit in the original data is contextual information on the speaker's gender and identity. For my work, it was necessary to make this information explicit. The file \"vowel-context.data\" adds the speaker's sex and identity as new features. The format of the data file is described below. Peter Turney peter@ai.iit.nrc.ca References = " ], "weight": 5 }, "qualities": { "NumberOfInstances": 990, "NumberOfFeatures": 14, "NumberOfClasses": 11, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 10, "NumberOfSymbolicFeatures": 4, "MaxStdDevOfNumericAtts": 1.1752720041815217, "MinorityClassPercentage": 9.090909090909092, "PercentageOfNumericFeatures": 71.42857142857143, "Quartile3MeansOfNumericAtts": 0.5441732323232322, "CfsSubsetEval_DecisionStumpAUC": 0.8608742985409651, "RandomTreeDepth2AUC": 0.8294657687991022, "J48.00001.ErrRate": 0.31313131313131315, "MeanAttributeEntropy": 1.9668246198572636, "MinorityClassSize": 90, "PercentageOfSymbolicFeatures": 28.57142857142857, "Quartile3MutualInformation": 0, "CfsSubsetEval_DecisionStumpErrRate": 0.30505050505050507, "RandomTreeDepth2ErrRate": 0.3212121212121212, "J48.00001.Kappa": 0.6555555555555556, "MeanKurtosisOfNumericAtts": -0.39142721501684635, "NaiveBayesAUC": 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