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Logistic Regression Realized with Artificial Neuron and Estimation Formulas

Korohoda P., Grabska-Chrząstowska J.

Image Processing & Communications

2012

Vol. 17, no. 4

265--274

In the paper an experiment is described, that was designed and conducted to verify hypothesis that artificial neuron with sigmoidal activation function can efficiently solve the task of logistic regression in the case when the explaining variable is one-dimensional, and the explained variable is binomial. Computations were performed with 12 sets of statistical parameters, assumed for the generation of 65356 sets of data in each case. Comparative analysis of the obtained results with use of the reference values for the regression coefficients indicated that the investigated neuron can satisfactory perform the task, with efficiency similar to that obtained with classical logistic regression algorithm, when the teaching sets of input data, corresponding with output values 0 and 1, do not allow for simple separation. Moreover, it has been discovered that the simple formulas estimating the statistical distributions parameters from the samples, offer statistically superior assessment of the regression coefficient parameters.

An analysis of a multidimensional dataset of an epidemic study using soft computing tools - a pilot study

Handri S., Nomura S., Irfan A. C.M., Fukuda S., Yamano E., Watanabe Y.

Journal of Medical Informatics & Technologies

2009

Vol. 13

107--110

Two contrasting approaches toward an epidemic study were illustrated as a pilot study; the regression analysis which is rather conventional methodology used in the past/present epidemic studies, and the other is the classifier analysis which is in the soft computing toolbox. The dataset we used for this study is obtained from a part of a cohort study which principally focused on a fatigue syndrome of the elementary and junior high school educates. In the classifier analysis we employed a major supervised machine-learning algorithm, K-Nearest Neighbour (K-NN), coupled with Principal Component Analysis (PCA). As a result, the performance that was found by cross validation method in the classifier analysis provides better results than that of the regression analysis. Finally we discussed the availability of both analyses with referring the technical and conceptual limitation of both approaches.