Once you have decided on a problem to solve using neural networks, you will need to gather data for training purposes. The training data set includes a number of cases, each containing values for a range of input and output variables. The first decisions you will need to make are: which variables to use, and how many (and which) cases to gather.

The choice of variables (at least initially) is guided by intuition. Your own expertise in the problem domain will give you some idea of which input variables are likely to be influential. As a first pass, you should include any variables that you think could have an influence - part of the design process will be to whittle this set down.

Neural networks process numeric data in a fairly limited range. This presents a problem if data is in an unusual range, if there is missing data, or if data is non-numeric. Fortunately, there are methods to deal with each of these problems. Numeric data is scaled into an appropriate range for the network, and missing values can be substituted for using the mean value (or other statistic) of that variable across the other available training cases (see Bishop, 1995).

Handling non-numeric data is more difficult. The most common form of non-numeric data consists of nominal-value variables such as Gender={Male, Female}. Nominal-valued variables can be represented numerically. However, neural networks do not tend to perform well with nominal variables that have a large number of possible values.

For example, consider a neural network being trained to estimate the value of houses. The price of houses depends critically on the area of a city in which they are located. A particular city might be subdivided into dozens of named locations, and so it might seem natural to use a nominal-valued variable representing these locations. Unfortunately, it would be very difficult to train a neural network under these circumstances, and a more credible approach would be to assign ratings (based on expert knowledge) to each area; for example, you might assign ratings for the quality of local schools, convenient access to leisure facilities, etc.

Other kinds of non-numeric data must either be converted to numeric form, or discarded. Dates and times, if important, can be converted to an offset value from a starting date/time. Currency values can easily be converted. Unconstrained text fields (such as names) cannot be handled and should be discarded.

The number of cases required for neural network training frequently presents difficulties. There are some heuristic guidelines, which relate the number of cases needed to the size of the network (the simplest of these says that there should be ten times as many cases as connections in the network). Actually, the number needed is also related to the (unknown) complexity of the underlying function which the network is trying to model, and to the variance of the additive noise. As the number of variables increases, the number of cases required increases nonlinearly, so that with even a fairly small number of variables (perhaps fifty or less) a huge number of cases are required. This problem is known as “the curse of dimensionality,” and is discussed further later in this chapter.

For most practical problem domains, the number of cases required will be hundreds or thousands. For very complex problems more may be required, but it would be a rare (even trivial) problem which required less than a hundred cases. If your data is sparser than this, you really don’t have enough information to train a network, and the best you can do is probably to fit a linear model. If you have a larger, but still restricted, data set, you can compensate to some extent by forming an ensemble of networks, each trained using a different resampling of the available data, and then average across the predictions of the networks in the ensemble.

Many practical problems suffer from data that is unreliable: some variables may be corrupted by noise, or values may be missing altogether. Neural networks are also noise tolerant. However, there is a limit to this tolerance; if there are occasional outliers far outside the range of normal values for a variable, they may bias the training. The best approach to such outliers is to identify and remove them (either discarding the case, or converting the outlier into a missing value). If outliers are difficult to detect, a city block error function (see Bishop, 1995) may be used, but this outlier-tolerant training is generally less effective than the standard approach.