Generating term weighting schemes through genetic programming

Abstract

Term-Weighting Scheme (TWS) is an important step in text classification. It determines how documents are represented in Vector Space Model (VSM). Even though state-of-the-art TWSs exhibit good behaviors, a large number of new works propose new approaches and new TWSs that improve performances. Furthermore, it is still difficult to tell which TWS is well suited for a specific problem. In this paper, we are interested in automatically generating new TWSs with the help of evolutionary algorithms and especially genetic programming (GP). GP evolves and combines different statistical information and generates a new TWS based on the performance of the learning method. We experience the generated TWSs on three well-known benchmarks. Our study shows that even early generated formulas are quite competitive with the state-of-the-art TWSs and even in some cases outperform them.

A New Term Frequency with Gaussian Technique for Text Classification and Sentiment Analysis

“…Increasing the accuracy of text classification is a challenging issue for researchers. The accuracy of text classification and sentiment analysis depends on the selected term weighting technique, dataset, and ML technique [8][9]. An effective technique must be able to determine the appropriate weights for the words in a text.…”