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Krishna Prasad, M.H.M.
- Multi-Tree Classification for Uncertain Markov Random Fields
Authors
1 Dept. of Computer Science and Engineering, JNTUK UCEV, Vizianagaram
2 Dept. of Information Technology, JNTUK UCEV, Vizianagaram
Source
International Journal of Computational Intelligence Research, Vol 9, No 1 (2013), Pagination: 1-6Abstract
Feature generation algorithms for searching globally useful features using traditional Markov network structures is now a days in wide practice. The composition of a Markov network can be represented one of two ways. The first approach is to treat this task as a global search problem. However, these algorithms are slow as they require running the expensive operation of weight learning many times. The second approach involves learning a set of local models and then combining them into a global model. However, it can be computationally expensive to learn the local models for datasets that contain a large number of variables and/or examples.
In this paper, we follow a third approach that views Markov network structure learning as a feature generation problem. For this we used an algorithm DTSL (Decision Tree Structured Learner) which combines a data-driven, adhoc-to-generic search strategy with randomization for quickly generating a large set of candidate features that all have support in the data. In addition to that it uses weight learning, using forest of uncertain decision trees to select a subset of generated features for making feature generation process more accurate and effective.
Keywords
Markov Networks, Rough Decision Trees, Likelihood, DTSL, Inference
References
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