Data classification from the inverse n(th) power gravitation
By the inverse n(th) power gravitation in physics, a novel classifier for data classification, called the I-n-PG classifier, is introduced. In the I-n-PG model, training samples from each class are regarded as a system of particles and an I-n-PG field is defined for each class. A test sample belongs to the class whose particle system has the maximum I-n-PG to this test sample. Experiments on large numbers of real data show that the I-n-PG classifier can provide a good classification performance. Compared with the nearest neighbor classifier and SVM, performances of the I-n-PG classifier are always superior/close to the better one of them, and thus the I-n-PG classifier has also a wide range of adaptability to data sets.
data classification inverse n(th) power gravitation