Top Document: comp.ai.neuralnets FAQ, Part 2 of 7: Learning Previous Document: How does illconditioning affect NN training? Next Document: Why not code binary inputs as 0 and 1? See reader questions & answers on this topic!  Help others by sharing your knowledge First, consider unordered categories. If you want to classify cases into one of C categories (i.e. you have a categorical target variable), use 1ofC coding. That means that you code C binary (0/1) target variables corresponding to the C categories. Statisticians call these "dummy" variables. Each dummy variable is given the value zero except for the one corresponding to the correct category, which is given the value one. Then use a softmax output activation function (see "What is a softmax activation function?") so that the net, if properly trained, will produce valid posterior probability estimates (McCullagh and Nelder, 1989; Finke and Müller, 1994). If the categories are Red, Green, and Blue, then the data would look like this: Category Dummy variables   Red 1 0 0 Green 0 1 0 Blue 0 0 1 When there are only two categories, it is simpler to use just one dummy variable with a logistic output activation function; this is equivalent to using softmax with two dummy variables. The common practice of using target values of .1 and .9 instead of 0 and 1 prevents the outputs of the network from being directly interpretable as posterior probabilities, although it is easy to rescale the outputs to produce probabilities (Hampshire and Pearlmutter, 1991, figure 3). This practice has also been advocated on the grounds that infinite weights are required to obtain outputs of 0 or 1 from a logistic function, but in fact, weights of about 10 to 30 will produce outputs close enough to 0 and 1 for all practical purposes, assuming standardized inputs. Large weights will not cause overflow if the activation functions are coded properly; see "How to avoid overflow in the logistic function?" Another common practice is to use a logistic activation function for each output. Thus, the outputs are not constrained to sum to one, so they are not admissible posterior probability estimates. The usual justification advanced for this procedure is that if a test case is not similar to any of the training cases, all of the outputs will be small, indicating that the case cannot be classified reliably. This claim is incorrect, since a test case that is not similar to any of the training cases will require the net to extrapolate, and extrapolation is thoroughly unreliable; such a test case may produce all small outputs, all large outputs, or any combination of large and small outputs. If you want a classification method that detects novel cases for which the classification may not be reliable, you need a method based on probability density estimation. For example, see "What is PNN?". It is very important not to use a single variable for an unordered categorical target. Suppose you used a single variable with values 1, 2, and 3 for red, green, and blue, and the training data with two inputs looked like this:  1 1  1 1  1 1  1 1   X   3 3 2 2  3 3 2  3 3 2 2  3 3 2 2  + Consider a test point located at the X. The correct output would be that X has about a 5050 chance of being a 1 or a 3. But if you train with a single target variable with values of 1, 2, and 3, the output for X will be the average of 1 and 3, so the net will say that X is definitely a 2! If you are willing to forego the simple posteriorprobability interpretation of outputs, you can try more elaborate coding schemes, such as the errorcorrecting output codes suggested by Dietterich and Bakiri (1995). For an input with categorical values, you can use 1of(C1) coding if the network has a bias unit. This is just like 1ofC coding, except that you omit one of the dummy variables (doesn't much matter which one). Using all C of the dummy variables creates a linear dependency on the bias unit, which is not advisable unless you are using weight decay or Bayesian learning or some such thing that requires all C weights to be treated on an equal basis. 1of(C1) coding looks like this: Category Dummy variables   Red 1 0 Green 0 1 Blue 0 0 If you use 1ofC or 1of(C1) coding, it is important to standardize the dummy inputs; see "Should I standardize the input variables?" "Why not code binary inputs as 0 and 1?" for details. Another possible coding is called "effects" coding or "deviations from means" coding in statistics. It is like 1of(C1) coding, except that when a case belongs to the category for the omitted dummy variable, all of the dummy variables are set to 1, like this: Category Dummy variables   Red 1 0 Green 0 1 Blue 1 1 As long as a bias unit is used, any network with effects coding can be transformed into an equivalent network with 1of(C1) coding by a linear transformation of the weights, so if you train to a global optimum, there will be no difference in the outputs for these two types of coding. One advantage of effects coding is that the dummy variables require no standardizing, since effects coding directly produces values that are approximately standardized. If you are using weight decay, you want to make sure that shrinking the weights toward zero biases ('bias' in the statistical sense) the net in a sensible, usually smooth, way. If you use 1 of C1 coding for an input, weight decay biases the output for the C1 categories towards the output for the 1 omitted category, which is probably not what you want, although there might be special cases where it would make sense. If you use 1 of C coding for an input, weight decay biases the output for all C categories roughly towards the mean output for all the categories, which is smoother and usually a reasonable thing to do. Now consider ordered categories. For inputs, some people recommend a "thermometer code" (Smith, 1996; Masters, 1993) like this: Category Dummy variables   Red 1 1 1 Green 0 1 1 Blue 0 0 1 However, thermometer coding is equivalent to 1ofC coding, in that for any network using 1ofC coding, there exists a network with thermometer coding that produces identical outputs; the weights in the thermometerencoded network are just the differences of successive weights in the 1ofCencoded network. To get a genuinely ordinal representation, you must constrain the weights connecting the dummy variables to the hidden units to be nonnegative (except for the first dummy variable). Another approach that makes some use of the order information is to use weight decay or Bayesian learning to encourage the the weights for all but the first dummy variable to be small. It is often effective to represent an ordinal input as a single variable like this: Category Input   Red 1 Green 2 Blue 3 Although this representation involves only a single quantitative input, given enough hidden units, the net is capable of computing nonlinear transformations of that input that will produce results equivalent to any of the dummy coding schemes. But using a single quantitative input makes it easier for the net to use the order of the categories to generalize when that is appropriate. Bsplines provide a way of coding ordinal inputs into fewer than C variables while retaining information about the order of the categories. See Brown and Harris (1994) or Gifi (1990, 365370). Target variables with ordered categories require thermometer coding. The outputs are thus cumulative probabilities, so to obtain the posterior probability of any category except the first, you must take the difference between successive outputs. It is often useful to use a proportionalodds model, which ensures that these differences are positive. For more details on ordered categorical targets, see McCullagh and Nelder (1989, chapter 5). References: Brown, M., and Harris, C. (1994), Neurofuzzy Adaptive Modelling and Control, NY: Prentice Hall. Dietterich, T.G. and Bakiri, G. (1995), "Errorcorrecting output codes: A general method for improving multiclass inductive learning programs," in Wolpert, D.H. (ed.), The Mathematics of Generalization: The Proceedings of the SFI/CNLS Workshop on Formal Approaches to Supervised Learning, Santa Fe Institute Studies in the Sciences of Complexity, Volume XX, Reading, MA: AddisonWesley, pp. 395407. Finke, M. and Müller, K.R. (1994), "Estimating aposteriori probabilities using stochastic network models," in Mozer, M., Smolensky, P., Touretzky, D., Elman, J., and Weigend, A. (eds.), Proceedings of the 1993 Connectionist Models Summer School, Hillsdale, NJ: Lawrence Erlbaum Associates, pp. 324331. Gifi, A. (1990), Nonlinear Multivariate Analysis, NY: John Wiley & Sons, ISBN 0471926205. Hampshire II, J.B., and Pearlmutter, B. (1991), "Equivalence proofs for multilayer perceptron classifiers and the Bayesian discriminant function," in Touretzky, D.S., Elman, J.L., Sejnowski, T.J., and Hinton, G.E. (eds.), Connectionist Models: Proceedings of the 1990 Summer School, San Mateo, CA: Morgan Kaufmann, pp.159172. Masters, T. (1993). Practical Neural Network Recipes in C++, San Diego: Academic Press. McCullagh, P. and Nelder, J.A. (1989) Generalized Linear Models, 2nd ed., London: Chapman & Hall. Smith, M. (1996). Neural Networks for Statistical Modeling, Boston: International Thomson Computer Press, ISBN 1850328420. User Contributions:Comment about this article, ask questions, or add new information about this topic:Top Document: comp.ai.neuralnets FAQ, Part 2 of 7: Learning Previous Document: How does illconditioning affect NN training? Next Document: Why not code binary inputs as 0 and 1? Part1  Part2  Part3  Part4  Part5  Part6  Part7  Single Page [ Usenet FAQs  Web FAQs  Documents  RFC Index ] Send corrections/additions to the FAQ Maintainer: saswss@unx.sas.com (Warren Sarle)
Last Update March 27 2014 @ 02:11 PM
