An Evolving Oblique Decision Tree Ensemble Architecture for Continuous Learning Applications
Ioannis T. Christou, Sofoklis Efremidis · 2007
We present a system architecture for evolving classifier ensembles of oblique decision trees for continuous or online learning applications. In continuous learning, the classification system classifies new instances for which after a short while the true class label becomes known and the system then receives this feedback control to improve its future predictions. We propose oblique decision trees as base classifiers using Support Vector Machines in order to compute the optimal separating hyper-plane for branching tests using subsets of the numerical attributes of the problem. The resulting decision trees maintain their diversity through the inherent instability of the decision tree induction process. We then describe an evolutionary process by which the population of base classifiers evolves during run-time to adapt to the newly seen instances. A latent set of base-classifiers is maintained as a secondary classifier pool, and an instance from the latent set replaces the currently active classifier whenever certain criteria are met. We discuss motivation behind this architecture, algorithmic details and future directions for this research.