A new network-base high-level data classification methodology (Quipus) by modeling attribute-attribute interactions
This is an incremental improvement for high-level data classification methods.
The paper tackles the problem of hidden patterns between attributes in high-level classification algorithms by proposing a new network building methodology based on attribute-attribute interactions that eliminates the need for normalization. The results show this approach improves the accuracy of the classification algorithm based on betweenness centrality.
High-level classification algorithms focus on the interactions between instances. These produce a new form to evaluate and classify data. In this process, the core is a complex network building methodology. The current methodologies use variations of kNN to produce these graphs. However, these techniques ignore some hidden patterns between attributes and require normalization to be accurate. In this paper, we propose a new methodology for network building based on attribute-attribute interactions that do not require normalization. The current results show us that this approach improves the accuracy of the high-level classification algorithm based on betweenness centrality.