Using a Probabilistic Neural Network for a Large Multi-label Problem

Abstract

Abstract

Particle Swarm Optimization Applied to Parameters Learning of Probabilistic Neural Networks for Classification of Economic Activities

“…Whereas, in the previous approach, we need to build many neural networks (83 in that case) which complicate the process of optimization. The results achieved in (Oliveira et al, 2008) using the proposed PNN were better than the achieved using the Multi-label k-Nearest Neighbors (ML-kNN) algorithm. The ML-kNN was considered to be the best algorithm for all the database used in (Zhang & Zhou, 2007).…”

“…Although it has found a reasonable value for the Recall, the value for the Precision was very low, since almost every neural networks returned at least one class to each instance of test. A PNN with a slightly modified architecture to treat problems of multi-label classification was proposed in (Oliveira et al, 2008). Such neural network presents advantage over the array of small standard PNN approach, used in , because only one PNN is used to solve the problem of multi-label classification.…”

“…The cited previous works used the same database that we present in this work, but the division of the database was performed in a different way for each work, making it difficult conducting a comparison of results among them. However, in this work we will divide the database in a similar way to used in (Oliveira et al, 2008), making possible a comparison among results. Another very close multi-label problem to one we are presenting in this paper, concern with the economic activities classification, is that of patent categorization (Li et al, 2007).…”

“…Comparing the results found in this tables we noticed that there weren't significant differences among them, this indicates that the proposed PNN presents certain robustness on the dataset used for training/validation. (Oliveira et al, 2008) is done in Table 6. The results mentioned are the mean of the four subsets for each metric, and for those in bold are the best results found for each one of the metrics.…”