1. Results Comparison with existing approaches on benchmark datasets Evaluation on a uveal melanoma datasetEvaluation on the two-spiral dataset Evaluation on strabismus (squint) dataset Conclusions The evaluation of MERBIS on artificial datasets, real clinical datasets, and benchmark datasets has shown that the system produces comprehensible classifiers of low complexity, which perform well on unseen data. Acknowledgments This research has been supported by the Computer Science Department of the University of Liverpool. MERBIS - A Multi-Objective Evolutionary Rule Base Induction System C Setzkorn*, AFG Taktak, AC Fisher, BE Damato # and A Chandna Dept. of Clinical Engineering, St Paul’s Eye Unit #, Royal Liverpool Children’s NHS Trust ‡, RLBUHT (C.Setzkorn@rlbuht.nhs.uk) Summary MERBIS is an artificial intelligence approach for classification which simulates the Darwinian principle of the survival of the fittest within a computer. MERBIS allows the extraction of simple, precise, and general classification models from existing data. The ‘fitness’ of a model is determined by its simplicity and precision. Introduction This research focuses upon the induction of classification models from data. Existing methods from disciplines such as statistics, machine learning and artificial neural networks require a great deal of expertise to generate models and to understand them. This can be disadvantageous, especially when the constructed models are used to formulate general hypotheses about the observed domain. To alleviate these problems, we induce fuzzy classification rule systems from datasets using a multi- objective evolutionary algorithm. Fuzzy classification rule systems correspond to a linguistic knowledge representation form that can easily be understood. Multi- objective evolutionary algorithms allow the optimisation of several objectives, such as the fit of the models to the dataset and their complexity. Methods Why Multi-Objective Evolutionary Algorithms? Advantages: Generate a set of trade-off solutions in a single run. Applicable to large and complex search spaces. Deals with incommensurable objectives. Unsusceptible to the shape of trade-off surface Alleviates over-fitting and reduces model complexity Disadvantages: Computational expensive. No guaranteed convergence. Classification using fuzzy rule systems – example iris dataset Validation The approach has been evaluated on artificial datasets and on real clinical data. In addition, it has been compared with existing approaches using benchmark datasets and 10-fold stratified cross-validation. Figure 2: Scatter diagrams of the iris dataset. Figure 3: A rule system generated for the iris data. Figure 4: Decision surfaces and contour plots of the rule system depicted in Figure 3. Decrease Complexity Increase area under receiver-operating curve Figure 1: Set of trade-off solutions. Red points are so- called non-dominated solutions (models) Table 1: Comparison of MERBIS with three existing approaches on several benchmark datasets. Figure 5: The generated model aims to predict the loss of an eye due to uveal melanoma. Decision surface (left) and contour plot (right) for a dataset related to uveal melanoma. Only two rules were used. The final model only deployed two out of the original twelve predictors. Figure 8: Scatter diagrams of the strabismus (squint) data. Figure 9: A rule system generated for the strabismus (squint) data. Figure 10: Decision surfaces and contour plots of the rule system depicted in Figure 9. Figure 6: Decision surface for the spiral dataset. The spiral dataset is a complicated artificial problem C linical E ngineering Royal Liverpool University Hospital The ‘iris dataset’ consists of hundred and fifty samples that represent flowers from the iris species setosa, versicolor, and virginica and is a classic benchmark problem. The dataset contains fifty samples from each flower species and each sample has four feature values: sepal length, sepal width, petal length and petal width. Strabismus problem: Differential diagnosis of squint into eight types (classes: 4 hypertropic and 4 hypotropic) using ten orthoptic measures (features) per patient.