Dynamic Function Generation for Text Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{gerber:2024:CEC,

• author = "Mia Gerber and Nelishia Pillay",
• title = "Dynamic Function Generation for Text Classification",
• booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2024",
• editor = "Bing Xue",
• address = "Yokohama, Japan",
• month = "30 " # jun # " - 5 " # jul,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Machine learning algorithms, Reviews, Heuristic algorithms, Neural networks, Text categorization, Subspace constraints, Germanium, dynamic function generation, text classification, grammatical evolution",
• isbn13 = "979-8-3503-0837-2",
• DOI = "doi:10.1109/CEC60901.2024.10611777",
• abstract = "Genetic programming and its variants, such as grammatical evolution, have been predominantly used for generating functions for machine learning techniques, such as loss or activation functions for neural networks and choice functions for the Fuzzy ART algorithm. These functions are evolved offline prior to the execution of the neural network and remains the same during the execution of the learning algorithm. We refer to this as static function generation (SFG). This study examines generating these functions in real-time at different points during the execution of the machine learning algorithm. We refer to this as dynamic function generation (DFG). Grammatical evolution (GE) is used to generate the function. Each function is generated at every $m$ epochs of the learning algorithm. Furthermore, the grammar used by GE also changes every $g$ generations of the GE algorithm. A selection perturbative hyper-heuristic is used to determine the options to include in the grammar. In previous work the effectiveness of using GE to evolve the choice function for the Fuzzy Art algorithm was shown. We use this as a case study to investigate DFG given the success of generating choice functions for this learning algorithm in previous work. However, DFG can be used with any neural network learning algorithm. Static and dynamic function generation is evaluated for text classification using the Enron, SMS Spam, Chat GPT tweets, IMDB movie reviews and Amazon product reviews datasets. DFG improved on the performance of SFG for all datasets. Additionally, DFG was found to be competitive with the state of the art and improved on the best known results for the SMS Spam and Chat GPT Tweets datasets.",
• notes = "also known as \cite{10611777}

  WCCI 2024",

}

Genetic Programming entries for Mia Gerber Nelishia Pillay

Citations