Lookahead selective sampling for incomplete data - Abdallah, Loai; Shimshoni, Ilan (Zielona Góra: Uniwersytet Zielonogórski)
Missing values in data are common in real world applications. There are several methods that deal with this problem. In this paper we present lookahead selective sampling (LSS) algorithms for datasets with missing values. We developed two versions of selective sampling. The first one integrates a distance function that can measure the similarity between pairs of incomplete points within the framework of the LSS algorithm. The second algorithm uses ensemble clustering in order to represent the data in a cluster matrix without missing values and then run the LSS algorithm based on the ensemble clustering instance space (LSS-EC).
Keywords: selective sampling; missing values; ensemble clustering