DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Unbalanced Classification

This is a common problem that we find in areas such as fraud, rare disease diagnosis, network intrusion, and others. The problem is that one class is very much underrepresented in the data. For example, cases of fraud in a very large medical insurance dataset are perhaps less than 1%. In compliance work where claims are being reviewed for compliance, often the number of claims that require adjustment is perhaps only 10%. In such circumstances, if we build a model in the usual way, where the aim is to minimise error rates, we can build the most accurate model to say that there is no fraud, and the model is up to 99% accurate, but of very little use.

Data mining of unbalanced datasets will involve adjustments to the modelling in some way. One approach is to down sample the majority case to even up the classes. Alternatively, we might over sample entities from the rare class and by so doing increase the weight of the minorities!

We illustrate two approaches to dealing with unbalanced datasets in See Chapter . There, one approach is to modify the weights, and the second is to down sample to balance up the classes. Both have been found to be very effective approaches when coupled with random forests.

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