Most of the real world datasets suffer from the problem of missing data. It may lead data mining analysts to end with wrong inferences about data under study. Many researchers are working on this problem to introduce more sophisticated methods. Eventhough many methods are available, analysts are facing difficulty in searching a suitable method due to lack of knowledge about the methods and thei...

Metric distances and the more general concept of dissimilarities are widely used tools in instance-based learning methods and very especially in the nearestneighbor classification technique. This paper contributes to the design of general dissimilarity measures to increase their utility. The ability to understand the main properties of a hand-crafted dissimilarity measure and to alter them if n...

In this paper, we present a procedure to estimate missing preference values when dealing with pairwise comparison and heterogeneous information. The procedure attempts to estimate the missing information in an expert's incomplete fuzzy preference relation using only the preference values provided by that particular expert. Our procedure to estimate missing values can be applied to incomplete fu...

The 1R procedure for machine learning is a very simple one that proves surprisingly effective on the standard datasets commonly used for evaluation. This paper describes the method and discusses two areas that can be improved: the way that intervals are formed when discretizing continuously-valued attributes, and the way that missing values are treated. Then we show how the algorithm can be ext...

Numerous learning tasks involve incomplete or connicting attributes. Most algorithms that automatically nd a set of fuzzy rules are not well suited to tolerate missing values in the input vector, and the usual technique to substitute missing values by their mean or another constant value can be quite harmful. In this paper a technique is proposed to tolerate missing values during the classiicat...

Missing data pose a potential threat to learning and classification in that they may compromise the ability of a system to develop robust, generalized models of the environment in which they operate. This investigation reports on the effects of three approaches to covering these data using an XCS-style learning classifier system. Using fabricated datasets representing a wide range of missing va...

Actually the growing volume of data provided by different sources some times may present inconsistencies, the data could be incomplete with lack of values or containing aggregate data, noisy containing errors or outliers, etc. Then data cleaning consist in filling missing values, smooth noisy data, identify or remove outliers and resolve inconsistencies. In more general definition, data cleanin...

This paper develops methodology for predicting faecal coliform values in waterways when some of the data are missing, and some of the data are left-censored. Such predictions are important in predicting when bathing is safe in specific areas of Sydney harbor. The approach taken in the paper makes use of spatial information to improve these predictions.

An application of the KL procedure for gappy data is presented to extend the well known factorization based algorithm for multi-image projective structure and motion. The predominant problem with factorization based techniques is that they require all points being reconstructed to be visible in all views, which is very unlikely in real scenes due to self occlusion. The approach presented here a...

One of the definitely unsolved main problems in molecular biology is the protein-protein functional association prediction problem. Genetic Programming (GP) is applied to this domain. GP evolves an expression, equivalent to a binary classifier, which predicts if a given pair of proteins interacts. We take advantages of GP flexibility, particularly, the possibility of defining new operations. In...