document: Hybrid decision tree In this paper , a hybrid learning approach named hybrid decision tree -LRB- HDT -RRB- is proposed . HDT simulates human reasoning by using symbolic learning to do qualitative analysis and using neural learning to do subsequent quantitative analysis . It generates the trunk of a binary HDT according to the binary information gain ratio criterion in an instance space defined by only original unordered attributes . If unordered attributes can not further distinguish training examples falling into a leaf node whose diversity is beyond the diversity-threshold , then the node is marked as a dummy node . After all those dummy nodes are marked , a specific feedforward neural network named FANNC that is trained in an instance space defined by only original ordered attributes is exploited to accomplish the learning task . Moreover , this paper distinguishes three kinds of incremental learning tasks . Two incremental learning procedures designed for example-incremental learning with different storage requirements are provided , which enables HDT to deal gracefully with data sets where new data are frequently appended . Also a hypothesis-driven constructive induction mechanism is provided , which enables HDT to generate compact concept descriptions
doc_bio_tags: B I I O O O O O B I I O B I I O O O O O O O O O B O O B I O O B I O O B I O O O B I O O O O O O O O O O O O B I I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B I O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O
extractive_keyphrases: hybrid decision tree hybrid learning approach reasoning symbolic learning qualitative analysis neural learning quantitative analysis binary information gain ratio criterion feedforward neural network fannc incremental learning storage requirements data sets
abstractive_keyphrases: hypothesis-driven constructive induction