Name
Title | ||
|---|---|---|
Connectionist construction of prototypes from decision trees for graph classification |
Abstract | ||
|---|---|---|
The paper addresses the question how learning class discriminations and learning characteristic class descriptions can be related in relational learning. We present the approach TRITOP/MATCHBOX combining the relational decision tree algorithm TRITOP with the connectionist approach MATCHBOX. TRITOP constructs efficiently a relational decision tree for the fast discrimination of classes of relational descriptions, while MatchBox is used for constructing class prototypes. Although TRITOP's decision trees perform very well in the classification task, they are difficult to understand and to explain. In order to overcome this disadvantage of decision trees in general, in a second step the decision tree is supplemented by prototypes. Prototypes are generalized graph theoretic descriptions of common substructures of those subclasses of the training set that are defined by the leaves of the decision tree. Such prototypes give a comprehensive and understandable description of the subclasses. In the prototype construction, the connectionist approach MATCHBOX is used to perform fast graph matching and graph generalisation, which are originally NP-complete tasks. The constructed prototypes can be used for classification in a decision list framework, where the decision list is constructed from the decision tree. |
Year | Venue | Keywords |
|---|---|---|
2003 | Intell. Data Anal. | graph classification,relational learning,characteristic class description,relational decision tree,relational description,decision tree,relational decision tree algorithm,approach tritop,decision list,connectionist construction,decision list framework,connectionist approach,machine learning,graphs,decision trees |
Field | DocType | Volume |
Decision tree,Pattern recognition,Statistical relational learning,Computer science,Decision list,Matching (graph theory),Artificial intelligence,ID3 algorithm,Decision tree learning,Alternating decision tree,Machine learning,Incremental decision tree | Journal | 7 |
Issue | Citations | PageRank |
2 | 2 | 0.43 |
References | Authors | |
14 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Geibel | 1 | 286 | 26.62 |
| Kristina Schä/dler | 2 | 2 | 0.43 |
| Fritz Wysotzki | 3 | 456 | 45.46 |