Automatic text categorization by a Granular Computing approach: Facing unbalanced data sets

Abstract

Text categorization is an interesting application of machine learning covering a wide range of possible applications, from document management systems to web mining. In designing such a system it is mandatory to correctly define both a suited preprocessing procedure and an effective document representation as closely related as possible to the semantic nature of document categories. To this aim, relying on a Granular Computing approach and considering a document as an ordered sequence of words, we propose a system able to automatically mine frequent terms, considering as a term not only a single word, but also a subsequence of (a few) consecutive words. The whole classification system is tailored to process sequences of atomic elements (i.e., encoded words) by means of an embedding procedure based on clustering methods. However, when dealing with unbalanced data sets, i.e. when classes are not evenly represented in the data set, the frequent substructures search procedure must be carefully designed. We prove the effectiveness of the system over a well-known benchmarking data set, achieving competitive test set classification accuracy results, with a remarkable low structural complexity of the synthesized classification models. © 2013 IEEE

Combining Piecewise Linear Regression and a Granular Computing Framework for Financial Time Series Classification

“…We generate N pat = 5 sequential patterns of mean length l sec = 8. The Training Set S tr is balanced (Possemato and Rizzi, 2013) and contains 100 patterned sequences (50 sequences per class) and 100 labeling sequences, while the Validation and Test Sets (S vs and S ts ) contain 50 patterned sequences (25 sequences per class) and 50 labeling sequences. The feature-based classifier adopted on the embedding space is based on the k-NN rule equipped with the Euclidean metric.…”