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KEEL-dataset - data set description

This section describes main characteristics of the westEast data set and its attributes:

General information

West East data set
TypeMulti instanceOriginReal world
Features 25(Real / Integer / Nominal)(24 / 0 / 1)
Instances213 Classes2
Missing values?No

Attribute description

Train_id{128, ... , 33 }C3=double[0.0, 1.0]L0=circle[0.0, 1.0]
C0[1.0, 4.0]C3=not_double[0.0, 1.0]L0=diamond[0.0, 1.0]
C1=bucket[0.0, 1.0]C4=arc[0.0, 1.0]L0=hexagon[0.0, 1.0]
C1=ellipse[0.0, 1.0]C4=flat[0.0, 1.0]L0=rectangle[0.0, 1.0]
C1=hexagon[0.0, 1.0]C4=jagged[0.0, 1.0]L0=triangle[0.0, 1.0]
C1=rectangle[0.0, 1.0]C4=none[0.0, 1.0]L0=utriangle[0.0, 1.0]
C1=u_shaped[0.0, 1.0]C4=peaked[0.0, 1.0]L1[0.0, 3.0]
C2=long[0.0, 1.0]C5[2.0, 3.0]Train_list1_order[0.0, 3.0]
C2=short[0.0, 1.0]Class{0, 1}

Additional information

The well-known East-West Challenge is originally an ILP problem. The problem consist of predicting whether a train is eastbound or westbound. A train (bag) contains a variable number of cars (instances) that have different shapes and carry different loads (instance-level attributes). As the standard MI assumption is asymmetric and it is not clear whether an eastbound train or a westbound train can be regarded as a positive example in the MI setting, we consider two MI versions of the data for our experiments. This datasets contains wesbound trains as positive examples.

In this section you can download some files related to the westEast data set:

  • The complete data set already formatted in KEEL format can be downloaded from herezip.gif.
  • A copy of the data set already partitioned by means of a 10-folds cross validation procedure can be downloaded from herezip.gif.
  • The header file associated to this data set can be downloaded from heretxt.png.

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