As one of the newest members in the field of artificial immune systems (AIS), the Dendritic Cell Algorithm (DCA) is based on behavioural models of natural dendritic cells (DCs). Unlike other AIS, the DCA does not rely on training data, instead domain or expert knowledge is required to predetermine the mapping between input signals from a particular instance to the three categories used by the D...

This study is concerned with whether it is possible to detect what information contained in the training data and background knowledge is relevant for solving the learning problem, and whether irrelevant information can be eliminated in preprocessing before starting the learning process. A case study of data preprocessing for a hybrid genetic algorithm shows that the elimination of irrelevant f...

Real world data sets usually have many features, which increases the complexity of data mining task. Feature selection, as a preprocessing step to the data mining, has been shown very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving comprehensibility. To find the optimal feature subsets is the aim of feature selection. Rough sets theory...

Aircraft engines are designed to be used during several tens of years. Ensuring a proper operation of engines over their lifetime is therefore an important and difficult task. The maintenance can be improved if efficients procedures for the understanding of data flows produced by sensors for monitoring purposes are implemented. This paper details such a procedure aiming at visualizing in a mean...

Some methods of fuzzy clustering need to use a priori knowledge about the number of fuzzy classes or some other information about the possible distribution of the clusters. A way to improve these methods is to use hierarchical clustering as a preprocessing of the data. This approach does not provide a simple partition of the data set, but a hierarchy of them. In this paper we define several mea...

The WMT17 Neural Machine Translation Training Task aims to test various methods of training neural machine translation systems. We describe the AFRL submission, including preprocessing and its knowledge distillation framework. Teacher systems are given factors for domain, case, and subword location. Student systems are given multiple teachers’ output and a subselected set of the training data d...

In general, the aim of our research is to adapt computational intelligence methods for computer-aided decision support in diagnosis and therapy of persons with Autism Spectrum Disorders (ASDs). In the paper, we are focusing on the data preprocessing step for cleaning a training data set for classifiers. An approach based on consistency factors is proposed.

The class of logconcave functions in Rn 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 an analysis technique for “smoothing” them out. This leads to efficient sampling algorithms with no assumptions on the local smoothness of the density function. Af...

This paper defines and establishes properties of a class of normalized radial visualizations (NRVs) that includes the RadViz mapping onto the two-dimensional unit disk. A NRV normalizes data to map highdimensional records into lower dimensional space, where records’ images are convex combinations of points called dimensional anchors. NRVs are radial visualizations because dimensional anchors ar...

Feature selection has been widely discussed as an important preprocessing step in data mining applications since it reduces a model's complexity. In this paper, limitations of several representative reduction methods are analyzed firstly, and then by distinguishing consistent objects form inconsistent objects, decision inclusion degree and its probability distribution function as a new measure ...