The class of logconcave functions in R is a common generalization of Gaussians and of indicator functions of convex sets. Motivated by the problem of sampling from a logconcave density function, we study their geometry and introduce a technique for “smoothing” them out. These results are applied to analyze two efficient algorithms for sampling from a logconcave distribution in n dimensions, wit...

An approach using clustering in combination with Rough Sets and neural networks was investigated for the purpose of gene discovery using leukemia data. A small number of genes with high discrimination power were found, some of which were not previously reported. It was found that subtle differences between very similar genes belonging to the same cluster, as well as the number of clusters const...

This paper describes an initial use of genetic programming as a discovery engine that derives two sets of information from hyper-spectral imagery. The first consists of a set of classification algorithms learned from the data. The second consists of reduced subsets of the most germane bands for use in a given classification, since not all spectral bands are of use in deriving a particular class...

The area of knowledge discovery and data mining is growing rapidly. A large number of methods are employed to mine knowledge. Many of the methods rely of discrete data. However, most of the datasets used in real application have attributes with continuous values. To make the data mining techniques useful for such datasets, discretization is performed as a preprocessing step of the data mining. ...

Tasks can be classi ed as either periodic which execute every some time units or vice versa as aperiodic. Sporadic tasks are a special case of aperiodic tasks which are guaranteed to have a minimum spacing between consecutive instances of the same task. Sporadic tasks sets can be handled by modeling them as periodic tasks and therefore we will concentrate on the former two types of tasks. Sched...

Feature selection and data sampling are two of the most important data preprocessing activities in the practice of data mining. Feature selection is used to remove less important features from the training data set, while data sampling is an effective means for dealing with the class imbalance problem. While the impacts of feature selection and class imbalance have been frequently investigated ...

Rough set based rule induction approaches have been studied intensively during past few years. However, classical rough set model cannot deal with incomplete data sets. There are two main categories dealing with this problem: the preprocessing methods and the extensions of rough set model. This paper focuses on the comparison of three strategies for dealing with incomplete data containing three...

Attribute reduction is considered as an important preprocessing step for pattern recognition, machine learning, and data mining. This paper provides a systematic study on attribute reduction with rough sets based on general binary relations. We define a relation information system, a consistent relation decision system, and a relation decision system and their attribute reductions. Furthermore,...

An important use of private data is to build machine learning classifiers. While there is a burgeoning literature on differentially private classification algorithms, we find that they are not practical in real applications due to two reasons. First, existing differentially private classifiers provide poor accuracy on real world datasets. Second, there is no known differentially private algorit...

We describe LMHS, an open source weighted partial maximum satisfiability (MaxSAT) solver. LMHS is a hybrid SAT-IP MaxSAT solver that implements the implicit hitting set approach to MaxSAT. On top of the main algorithm, LMHS offers integrated preprocessing, solution enumeration, an incremental API, and the use of a choice of SAT and IP solvers. We describe the main features of LMHS, and give emp...