Logistic Regression Classification for Uncertain Data

Logistic regression (LR) is a famous classification technique commonly used in statistics, machine learning, and data mining area of knowledge for learning a response of binary nature. It assumes that the data values are pre-determined precisely, but this is not true for all conditions. Uncertainty data arises in many applications because of data collection methodology as in repeated measures, outdated sources and imprecise measurement as in physical experiments. Studying this uncertainty data becomes area of interest for researchers nowadays. In uncertainty, the value of data item is mostly characterized by a multiple values. So, machine learning techniques are also required to manage an uncertain data. This paper studies the modification of LR technique to handle data with an uncertainty. Statistical inference and theory of probabilities are used to obtain single unbiased estimator that represents the multiple values sufficiently and efficiently. The Maximum Likelihood Estimators (MLE) and the Probabilities Density Function (PDF) are used to capture the uncertainty. Results of the Experiments on UCI data sets demonstrated that the uncertain LR classifier can be constructed successfully, and its accuracy can be improved by taking into consideration the uncertainty information.