Big Data Classification Using Augmented Decision Trees
This work addresses the need for interpretable models in big data classification, though it appears incremental as it builds on existing decision tree and ensemble techniques.
The paper tackles classification on big data by proposing an algorithm that combines decision trees with leaf-level classifiers to achieve accuracy comparable to ensemble methods while maintaining interpretability.
We present an algorithm for classification tasks on big data. Experiments conducted as part of this study indicate that the algorithm can be as accurate as ensemble methods such as random forests or gradient boosted trees. Unlike ensemble methods, the models produced by the algorithm can be easily interpreted. The algorithm is based on a divide and conquer strategy and consists of two steps. The first step consists of using a decision tree to segment the large dataset. By construction, decision trees attempt to create homogeneous class distributions in their leaf nodes. However, non-homogeneous leaf nodes are usually produced. The second step of the algorithm consists of using a suitable classifier to determine the class labels for the non-homogeneous leaf nodes. The decision tree segment provides a coarse segment profile while the leaf level classifier can provide information about the attributes that affect the label within a segment.