dbworld-subjects-stemmed

OpenML dataset with id 1563

No author found.

Full work available at URL: https://api.openml.org/data/v1/download/1593759/dbworld-subjects-stemmed.arff

Upload date: 1 June 2015

Dataset Characteristics

Number of classes: 2
Number of features: 230 (numeric: 0, symbolic: 230 and in total binary: 230 )
Number of instances: 64
Number of instances with missing values: 0
Number of missing values: 0

Description

Author: Michele Filannino Source: UCI Please cite:

Dataset:

DBworld e-mails data set Task: dbworld-subjects-stemmed

Source:

Michele Filannino, PhD University of Manchester Centre for Doctoral Training Email: filannim_AT_cs.man.ac.uk

Data Set Information:

I collected 64 e-mails from DBWorld newsletter and I used them to train different algorithms in order to classify between 'announces of conferences' and 'everything else'. I used a binary bag-of-words representation with a stopword removal pre-processing task before.

Attribute Information:

Each attribute corresponds to a precise word or stem in the entire data set vocabulary (I used bag-of-words representation).

Relevant Papers:

Michele Filannino, 'DBWorld e-mail classification using a very small corpus', Project of Machine Learning course, University of Manchester, 2011.

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