Abstract
The paper presents a decision algorithmic model called vector gravitational force model in the feature space. The algorithmic model, inspired by and similar to the Law of Universal Gravitation, is derived from the vector geometric analysis of the linear classifier and established in the feature space. Based on this algorithmic model, we propose a classification method called vector gravitational recognition. The proposed method is applied to the benchmark Glass Identification task in the UCI Database available from USA Forensic Science Service, and other two UCI benchmark tasks. The experimental and comparative results show that the proposed approach yields quite good results and outperforms some well known and recent approaches on the tasks, and other applications may benefit from ours.
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Acknowledgments
The author would like to express thanks to the website of UCI Knowledge Discovery in Databases Archive & UCI Machine Learning and the authors for their efforts in publishing their data in the Internet, and Prof. Xu Ji-sheng for his beneficial discussions. The author would like to extend his thanks to the editor(s) and reviewer(s) for their helpful suggestions.
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Appendix A: A 2-D data simulation
Appendix A: A 2-D data simulation
The records used here for simulation are chosen from some Chinese students. Each instance has two numeric attributes (Height (cm) and Weight (kg)) and one of two possible categories (Sex): male or female. The training data has four instances including two boys and two girls (S i ,i = 1,2,3,4), and the test data has two instances including one boy (M*)and one girl(F*).
The simulation results based on the proposed Vector Gravitational Force are listed in Table 11 and demonstrated in Fig. 3.
Illustration of the 2-D Data Simulation
From Table 11 and Fig. 3, we get and see: (M* → S 1) and (F* → S 4) by using the Vector Gravitational Force and both are correctly classified.
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Zong-chang, Y. A vector gravitational force model for classification. Pattern Anal Applic 11, 169–177 (2008). https://doi.org/10.1007/s10044-007-0091-9
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DOI: https://doi.org/10.1007/s10044-007-0091-9