Sequence Data Classification using Decision Trees with Novel Data Splitting Techniques
DOI:
https://doi.org/10.32628/CSEIT24102145Keywords:
complex sequence data, sequence decision tree, Global-Energy-Technique (GET), Maximum-Minus-Minimum-Energy- Technique (MMET), Maximum-Energy-Technique (MET), Square-Maximum-Energy-Technique (SMET), Vectorization-Based Approach, splitting attribute, sub-sequence, Sequence_Decision_TreeAbstract
Abstract: The research study in this paper aims to propose advanced research methodologies for classifying sequence data, addressing a fundamental challenging task in contemporary real time applications: the categorization of sequential information. Traditional classification approaches, such as Classification and Regression Trees (CART) and decision tree algorithms like C4.5, have been widely utilized in many real-time applications due to their attractive features such as classification accuracy, scalability, simplicity, interpretability, and general applicability. These attributes have solidified decision trees as benchmark models in both data mining and machine learning applications. However, standard C4.5 and CART are inherently limited to processing fixed-length vector data and are incapable of handling more complex data structures such as sequences, trees, graphs and special structures. To facilitate efficient and effective classification of sequence data, there is an increasing demand for cutting-edge, autonomous, highly intelligent models capable of real-time analysis. This particular study introduces four novel methodologies for decision tree-based sequence data classification: Global Energy Technique-1 (GET-1), Maximum Minus Minimum Energy Technique-2 (MMET-2), Maximum Energy Technique-3 (MET-3), and Square Maximum Energy Technique-4 (SMET-4). These special techniques leverage the concept of data energy or information value of data, which is analogous to its informational value, to enhance classification accuracy and efficiency.
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