In KDD procedure, to fill in missing data typically requires a very large investment of time and energy often 80% to 90% of a data analysis project is spent in making the data reliable enough so that the results can be trustful. In this paper, we propose a SVM regression based algorithm for filling in missing data, i.e. set the decision attribute (output attribute) as the condition attribute (i...

We consider a partially linear regression model with multivariate covariates and with responses that are allowed to be missing at random. This covers the usual settings with fully observed data and the nonparametric regression model as special cases. We first develop a test for additivity of the nonparametric part in the complete data model. The test statistic is based on the difference between...

The purpose of this paper is to suggest estimators for the parameters of spatial models containing a spatially lagged dependent variable, as well as spatially lagged independent variables, and an incomplete data set. The specifications allow for nonstationarity, and the disturbance process of the model is specified non-parametrically. We consider various scenarios concerning the pattern of miss...

The task of classification with incomplete data is a complex phenomena and its performance depends upon the method selected for handling the missing data. Missing data occur in datasets when no data value is stored for an attribute / feature in the dataset. This paper provides a brief overview to the problem of incomplete data handling techniques and discusses the various methods used with clas...

Averaged n-Dependence Estimators (AnDE) is a family of learning algorithms that range from low variance coupled with high bias through to high variance coupled with low bias. The asymptotic error of the lowest bias variant is the Bayes optimal. The AnDE family of algorithms have a training time that is linear with respect to the training examples, learn in a single pass through the data, suppor...