1. Results Classification - Comparison with existing approaches on benchmark datasets Survival Analysis – International multi-centre double-blind study Survival Analysis – Finding subgroups with significantly different survival curves Conclusions And Future Work MOEAs are versatile algorithms for successful model extraction MOEAs can produce models that perform well on real-world and benchmark data The approach will be tested on artificial benchmark data to determine under which conditions the approach performs well or not. Acknowledgments This research has been supported by the Computer Science Department of the University of Liverpool. Model Extraction Using Multi-Objective Evolutionary Algorithms C Setzkorn*, AFG Taktak*, BE Damato # Dept. of Clinical Engineering*, Ocular Oncology Centre #, RLBUHT (C.Setzkorn@csc.liv.ac.uk) Summary Multi-objective evolutionary algorithms (MOEAs) are powerful optimisation algorithms which simulate the Darwinian principle of the survival of the fittest within a computer. MOEAs can be used to extract models from data as they can optimise several objectives at the same time (e.g. the parameters and structure of the model) and are less susceptible to interaction effects and noise within the data. The present work uses a MOEA to extract models for classification and survival analysis. In addition, the MOEA is used to find subgroups of patients with significantly different survival curves. Introduction And Methods Structure Of A Multi-Objective Evolutionary Algorithm Genetic Operators And Selection 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 the trade-off surface Alleviates over-fitting and reduces model complexity Disadvantages: Computational expensive. No guaranteed convergence. Implementation of international multi-centre double-blind study Table 1: Comparison of the MOEA with three existing approaches on several benchmark datasets. C linical E ngineering Royal Liverpool University Hospital Figure 6: Decision surface for the spiral dataset. The spiral dataset is a complicated artificial problem Crossover Mutation Selection Figure 1: Structure of a multi-objective evolutionary algorithm. Figure 2: Genetic operators: crossover and mutation. They can change the structure and the parameters of models. Figure 3: Selection simulates the Darwinian principle of the survival of the fittest. Decrease complexity Increase fit to the data Figure 4: Set of trade-off solutions. Red points are so-called non-dominated solutions (models). Participants Hane/Paulo (UK), Patrizia (Italy), Ian (UK), Christian (UK), George (Greece) Elia (Italy), Azzam (UK) General Ocular Oncology Database (Geoconda) Assessors Referee Figure 5: Model extraction performed by five centres from three countries. The results are evaluated in a double-blind manner. Figure 8: Kaplan-Meier curves for the Leukemia data (solid line - Treatment, dashed line - Placebo) together with the estimates of the model (asterisks - Treatment, cross - Placebo). Figure 7: Results of an international multi-centre study. Performance measured by the CIndex. RBF is the MOEA approach. Figure 9: The produced models can also be used via the Internet. Figure 10: Kaplan-Meier curves of four generated subgroups with significantly different survival times (Log-Rank 320.0965, p < 0.0001). Figure 11: The MOEA was used to describe the four subgroups within the feature space. Initialise Population Randomly Apply Genetic Operators Fitness Evaluation Do It Again ? Output Archive Selection