Dataset (Classification)

JapaneseVowels

JapaneseVowels time series classification problem.

Example of a multivariate problem with unequal-length series.

Notes

Dimensionality: multivariate, 12 variables Series length: 7-29 (variable length) Train cases: 270 Test cases: 370 Number of classes: 9

A UCI Archive dataset. Nine Japanese male speakers were recorded saying the vowels ‘a’ and ‘e’. A 12-degree linear prediction analysis is applied to the raw recordings to obtain time series with 12 dimensions and varying lengths between 7 and 29. The classification task is to predict the speaker. Each instance is a transformed utterance with a single class label attached (labels 1 to 9).

Reference: M. Kudo, J. Toyama, and M. Shimbo. (1999). “Multidimensional Curve Classification Using Passing-Through Regions”. Pattern Recognition Letters, Vol. 20, No. 11-13, pages 1103-1111.

Dataset details: http://timeseriesclassification.com/description.php?Dataset=JapaneseVowels

Examples

>>> from sktime.datasets.classification import JapaneseVowels
>>> X, y = JapaneseVowels().load("X", "y")

Schnellstart

python

from sktime.datasets.classification.japanese_vowels import JapaneseVowels

estimator = JapaneseVowels(return_mtype='pd-multiindex')

Beispiele

>>> from sktime.datasets.classification import JapaneseVowels
>>> X, y = JapaneseVowels (). load ("X", "y")