Fast classification using sparse decision DAGs
This addresses the need for faster classification in applications like object detection and web ranking, offering a flexible alternative to cascades, though it is incremental in its approach.
The paper tackles the problem of improving classification speed by building sparse decision DAGs from base classifiers, using a Markov decision process to select classifiers data-dependently. The result is a method that is competitive with state-of-the-art cascade detectors on object-detection benchmarks, outperforms them with few base classifiers, and in a multi-class setup, significantly improves decision speed without harming performance on a web page ranking dataset.
In this paper we propose an algorithm that builds sparse decision DAGs (directed acyclic graphs) from a list of base classifiers provided by an external learning method such as AdaBoost. The basic idea is to cast the DAG design task as a Markov decision process. Each instance can decide to use or to skip each base classifier, based on the current state of the classifier being built. The result is a sparse decision DAG where the base classifiers are selected in a data-dependent way. The method has a single hyperparameter with a clear semantics of controlling the accuracy/speed trade-off. The algorithm is competitive with state-of-the-art cascade detectors on three object-detection benchmarks, and it clearly outperforms them when there is a small number of base classifiers. Unlike cascades, it is also readily applicable for multi-class classification. Using the multi-class setup, we show on a benchmark web page ranking data set that we can significantly improve the decision speed without harming the performance of the ranker.