Geração incremental de protótipos controlada por entropia para algoritmos de modelagem preditiva - Vasconcelos, Bruno Paulo de (2020)
The main proposals of this dissertation are modifying the GNG (Growing Neural Gas) algorithm for prototype generation from a new automatic stop method to ﬁnd the right amount of prototypes and also the creation of a prototype selection method called KPS with the goal of improving the accuracy in relation to just use the modiﬁed GNG. To create this methods were researched the algorithm operation and which techniques are used inside of it. Algorithms like kNN (k Nearest Neighbor), ENN (Edited Nearest Neighbor), DROP3 (Decremental Reduction Optimization Procedure 3), ATISA1 (Adaptive Threshold-based Instance Selection Algorithm 1) and RIS (Ranking-based Instance Selection) were studied in order to make a comparative study with the created methods. The project methodology consists in an exploratory study of the modiﬁed GNG and the prototype selection technique with real databases. The full results will be presented in experimental results and soon after will be made the conclusion, noting that the proposed method contributed to the improvement of accuracy.