Decision stream graph

Decision stream is a directed acyclic graph of decision rules for classification and regression tasks (Fig. 1). This decision tree based method [1] avoids the problem of data exhaustion in terminal nodes by merging of leaves from the same/different levels of predictive model.

Fig. 1. Decision stream: statistic-based merge of nodes from the same/different levels of predictive model.

Fig. 2. Binary decision stream and tree with the same quantity of nodes.

Decision stream provides:

– High accuracy due to the precise splitting of data with unpaired two-sample test statistics.

– Decrease of overfitting due to partition of data only into statistically representative groups.

– Reduction of complexity on every level of predictive model.

– Self-regulated depth of predictive model.

References

Ignatov, D.Yu.; Ignatov, A.D. (2017). "Decision Stream: Cultivating Deep Decision Trees". IEEE Ictai: 905–912. arXiv:1704.07657. Bibcode:2017arXiv170407657I. doi:10.1109/ICTAI.2017.00140. ISBN 978-1-5386-3876-7. S2CID 21864203.

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[1] Ignatov, D.Yu.; Ignatov, A.D. (2017). "Decision Stream: Cultivating Deep Decision Trees". IEEE Ictai: 905–912. arXiv:1704.07657. Bibcode:2017arXiv170407657I. doi:10.1109/ICTAI.2017.00140. ISBN 978-1-5386-3876-7. S2CID 21864203.