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include it!) A transform was sometimes applied to data when the data did not approximate the normal curve In general, the ANN performed better when the input data was normally distributed Other formal techniques for preprocessing included the calculation of intermediate functions and trends An example of an intermediate function is the use of ratios It is usually more meaningful and ef cient to present a ratio, such as miles per gallon, to the network than to input the individual components, such as miles traveled and gallons consumed Similarly, an algorithm is used to convert raw data into a form that captures a trend over time The following heuristics have been proposed for the use of binary and continuous data for training ANNs: 1 For naturally occurring groups, the binary categories are often the best method for determining the correlation 2 Continuous-valued inputs should never be used to represent unique concepts 3 For continuous value attributes, breaking them up into groups can be a mistake Among other heuristics is the leave-k-out technique, which involves the decision of how to handle input samples when relatively few examples are available The procedure is to train several networks (each with a different subset), including most of the examples, and then test each network with a different subset In enumerating the three general requirements for high-quality training data above, it was stated that the training data should not contain contradictory examples (ie, examples with identical inputs but different outputs) As a preprocessing issue, an algorithm was developed to deal with con icting data Con icting data exists when two or more input patterns are either identical or very similar, but the two inputs have completely different outputs Because the patterns being presented are contradictory, the ANN is unable to learn both patterns In an example, 65% of the original data le consisted of con icting data The cause was not attributed to faulty data, but to an incomplete modeling process When a suf cient number of elds are not included in the model to distinguish between the two output categories, a con icting-data situation exists and the network trains and tests poorly; however, the problem is not solved by eliminating portions of the data The solution involves reexamining the data model and including the necessary elds, a tedious task when performed manually To assist with this examination process, an algorithm was developed With the use of the algorithm to reconstruct he training data, we can obtain a 25% increase in network performance A set of studies focused on improving the data model for ANN training by analyzing the feedback obtained by interpreting the connection weights of the network s processing elements The nature of the ANN paradigm is such that the meaning of connection weights is opaque; therefore it is dif cult to assess the relative importance of the input factors used by an ANN to arrive at its conclusions.

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If such feedback were available, it might be possible to gain better insight into the types of inputs that could enhance the network effectiveness Several techniques have been developed that attempt to provide an interpretation of the connection weights In one method, the partition is used to make judgments about he relative importance of the various inputs The method consists of applying an equation that effectively partitions the middle-to-output layer weights of each middle (hidden-layer) node into components associated with each input node It has been suggested that this method can make ANNs as effective for modeling applications as for pattern matching A more manually intensive method has been proposed for interpreting the weights that entails iteratively presenting speci c isolated features to the trained network and assessing the network s reaction For example, after training the network to recognize seven letters of the alphabet, various features of the letters (such as the angular top of the letter A) were presented to the network, and the responses of each node were examined Through this method, the discriminating features were identi ed In both the above-mentioned methods, it was unclear to what degree the technique s applicability would extend to networks with different architectures and levels of complexity One of the most interesting methods of a weight-interpreting technique was the Knowledgetron (KT) The KT method uses a sophisticated algorithm to walk through the nodes of the network, interpret the connections, and generate a set of if then rules to match the behavior of the network These rules can then become the basis for a rule-based expert system It is particularly noteworthy that the KT technique was able to process the classic Iris data set successfully A set of only ve relatively simple rules were generated from ANN trained on the Iris data In fact, it was found that the generated rules actually outperformed the ANN from which they were generated Because the Iris data set is not particularly complex, it would be premature to assume that the KT technique would scale to more complex, real-world data sets; however, its performance provides evidence that further research in this area would be worthwhile From the perspective of the data issue, the production of rules provides a convenient mechanism for evaluating the importance and role of each input data element This could form the basis for a feedback-and-re ne iterative process to guide the selection and preprocessing of training data, which might result in improved ANN performance From the foregoing discussion, it can be observed that the range of techniques and possibilities for enhancing ANN effectiveness, even limiting the eld to consider only the data component, is broad and complex Many of these techniques require a level of expertise that may prohibit their use in practical situations; however, a growing body of research and applications in the area of hybrid intelligent systems could address this concern Many hybrid intelligent systems have been developed in recent years Expert systems and ANNs have characteristics that can complement each other and form the basis for powerful hybrid systems Several examples of expert systems used as.

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