We study random cutting down of a rooted tree and show that the number of cuts is equal (in distribution) to the number of records in the tree when edges (or vertices) are assigned random labels. Limit theorems are given for this number, in particular when the tree is a random conditioned Galton–Watson tree. We consider both the distribution when both the tree and the cutting (or labels) are ra...

This paper explores the utility of an ensemble decision-tree method called random forest, in comparison with the classic classification and regression trees (CART) algorithm, for forecasting ground-level ozone pollution in the Sydney metropolitan region. Statistical forecasting models are developed to provide daily ozone forecasts in November-March for three subregions, i.e., Sydney east, Sydne...

MOTIVATION What constitutes a subtle motif? Intuitively, it is a motif that is almost indistinguishable, in the statistical sense, from random motifs. This question has important practical consequences: consider, for example, a biologist that is generating a sample of upstream regulatory sequences with the goal of finding a regulatory pattern that is shared by these sequences. If the sequences ...

The classification problem is one of the important research subjects in the field of machine learning. However, most machine learning algorithms train a classifier based on the assumption that the number of training examples of classes is almost equal. When a classifier was trained on imbalanced data, the performance of the classifier declined clearly. For resolving the class-imbalanced problem...

We seek decision rules for prediction-time cost reduction, where complete data is available for training, but during prediction-time, each feature can only be acquired for an additional cost. We propose a novel random forest algorithm to minimize prediction error for a user-specified average feature acquisition budget. While random forests yield strong generalization performance, they do not ex...

This paper proposes, describes and evaluates T3C, a classification algorithm that builds decision trees of depth at most three, and results in high accuracy whilst keeping the size of the tree reasonably small. T3C is an improvement over algorithm T3 in the way it performs splits on continuous attributes. When run against publicly available data sets, T3C achieved lower generalisation error tha...

An induced forest of a graph G is an acyclic induced subgraph of G. The present paper is devoted to the analysis of a simple randomised algorithm that grows an induced forest in a regular graph. The expected size of the forest it outputs provides a lower bound on the maximum number of vertices in an induced forest of G. When the girth is large and the degree is at least 4, our bound coincides w...

A disorder or illness called heart failure results in the becoming weak damaged. In order to avoid early on, it is crucial understand causes of failure. Based on validation, two experimental processing steps will be applied dataset clinical records related Testing done first step utilizing six different classification algorithms, including K-nearest neighbor, neural network, random forest, deci...

Concept drift has potential in smart grid analysis because the socio-economic behaviour of consumers is not governed by the laws of physics. Likewise there are also applications in wind power forecasting. In this paper we present decision tree ensemble classification method based on the Random Forest algorithm for concept drift. The weighted majority voting ensemble aggregation rule is employed...