3.1 Data Sets

To evaluate the performance of Bagging and Boosting, we obtained a number of data sets from the University of Wisconsin Machine Learning repository as well as the UCI data set repository [Murphy Aha1994]. These data sets were hand selected such that they (a) came from real-world problems, (b) varied in characteristics, and (c) were deemed useful by previous researchers. Table 1 gives the characteristics of our data sets. The data sets chosen vary across a number of dimensions including: the type of the features in the data set (i.e., continuous, discrete, or a mix of the two); the number of output classes; and the number of examples in the data set. Table 1 also shows the architecture and training parameters used in our neural networks experiments.

  

Table 1: Summary of the data sets used in this paper. Shown are the number of examples in the data set; the number of output classes; the number of continuous and discrete input features; the number of input, output, and hidden units used in the neural networks tested; and how many epochs each neural network was trained.