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In data analysis problems where the data are represented by vectors of real numbers, it is often the case that some of the data points will have " missing values " , meaning that one or more of the entries of the vector that describes the data point is not known. In this paper, we propose a new approach to the imputation of missing binary values. The… (More)

This paper concerns classification by Boolean functions. We investigate the classification accuracy obtained by standard classification techniques on unseen points (elements of the domain, {0, 1}(n), for some n) that are similar, in particular senses, to the points that have been observed as training observations. Explicitly, we use a new measure of how… (More)

A probabilistic constrained stochastic programming problem is considered , where the underlying problem has linear constraints with random technology matrix. The rows of the matrix are assumed to be stochastically independent and normally distributed. For the convexity of the problem the quasi-concavity of the constraining function is needed that is ensured… (More)

Logical Analysis of Data (LAD) is a two-class learning algorithm which integrates principles of combinatorics, optimization, and the theory of Boolean functions. This paper proposes an algorithm based on mixed integer linear programming to extend the LAD methodology to solve multi-class classification problems, where One-vs-Rest (OvR) learning models are… (More)

Logical Analysis of Data (LAD) is a supervised learning algorithm which integrates principles of combinatorics, optimization and the theory of Boolean functions. Current implementations of LAD use greedy-type heuristics to select patterns to form an LAD model. In this paper we present a new approach based on integer programming and network flows to identify… (More)

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