Novelty detection using soft partitioning and hierarchical models

In this paper, we study novelty detection problem and introduce an online algorithm. The algorithm sequentially receives an observation, generates a decision and then updates its parameters. In the first step, to model the underlying distribution, algorithm constructs a score function. In the second step, this score function is used to make the final decision for the observed data. After thresholding procedure is applied, the final decision is made. We obtain the score using versatile and adaptive nested decision tree. We employ nested soft decision trees to partition the observation space in an hierarchical manner. Based on the sequential performance, we optimize all the components of the tree structure in an adaptive manner. Although this in time adaptation provides powerful modeling abilities, it might suffer from overfitting. To circumvent overfitting problem, we employ the intermediate nodes of tree in order to generate subtrees and we then combine them in an adaptive manner. The experiments illustrate that the introduced algorithm significantly outperforms the state of the art methods.