Improve High Level Classification with a More Sensitive metric and Optimization approach for Complex Network Building
This work addresses classification accuracy in machine learning, but it is incremental as it builds on existing complex network methods.
The paper tackles the problem of building complex networks for high-level classification by proposing a class-specific network creation method and an optimization approach, achieving up to 10% improvement in performance.
Complex Networks are a good approach to find internal relationships and represent the structure of classes in a dataset then they are used for High Level Classification. Previous works use K-Nearest Neighbors to build each Complex Network considering all the available samples. This paper introduces a different creation of Complex Networks, considering only sample which belongs to each class. And metric is used to analyze the structure of Complex Networks, besides an optimization approach to improve the performance is presented. Experiments are executed considering a cross validation process, the optimization approach is performed using grid search and Genetic Algorithm, this process can improve the results up to 10%.