The use of randomness in constructing decision tree ensembles has drawn much attention in the machine learning community. In general, ensembles introduce randomness to generate diverse trees and in turn they enhance ensembles’ predictive accuracy. Examples of such ensembles are Bagging, Random Forests and Random Decision Tree. In the past, most of the random tree ensembles inject various kinds ...

One challenge of biology, medicine, and economics is that the systems treated by these sciences have no perfect metronome in time and no perfect spatial architecture – crystalline or otherwise. Nonetheless, as if by magic, out of nothing but randomness one finds remarkably fine-tuned processes in time and remarkably fine-tuned structures in space. To understand this ‘miracle’, one might conside...

We consider two-player zero-sum games on graphs. These games can be classified on the basis of the information of the players and on the mode of interaction between them. On the basis of information the classification is as follows: (a) partial-observation (both players have partial view of the game); (b) one-sided complete-observation (one player has complete observation); and (c) complete-obs...

Humans can easily discriminate a randomly spaced from a regularly spaced visual pattern. Here, we demonstrate that observers can adapt to pattern randomness. Following their adaption to prolonged exposure to two-dimensional patterns with varying levels of physical randomness, observers judged the randomness of the pattern. Perceived randomness decreased (increased) following adaptation to high ...

UNLABELLED PREMISE OF THE STUDY Because of their limited length, xylem conduits need to connect to each other to maintain water transport from roots to leaves. Conduit spatial distribution in a cross section plays an important role in aiding this connectivity. While indices of conduit spatial distribution already exist, they are not well defined statistically. • METHODS We used point patte...

The spatial clustering of points from two or more classes (or species) has important implications in many fields and may cause segregation or association, which are two major types of spatial patterns between the classes. These patterns can be studied using a nearest neighbour contingency table (NNCT) which is constructed using the frequencies of nearest neighbour types. Three new multivariate ...

Although uncertainties exist in spatial data mining, they have not been paid much attention to. Uncertainty may have an influence on the confidential level, supportable level, and interesting level of spatial data mining. This paper proposes uncertainty-based spatial data mining. First, the concept is given in the integrated contexts of both uncertainty and spatial data mining. The inherent unc...

Outlier detection targets those exceptional data whose pattern is rare and lie in low density regions. In this paper, under the assumption of complete spatial randomness inside clusters, we propose an MDV (Multi-scale Deviation of the Volume) approach to identifying outliers. In addition to assigning an outlier score for each object, it directly outputs a crisp outlier set. It also offers a plo...