We introduce canonical correlation forests (CCFs), a new decision tree ensemble method for classification. Individual canonical correlation trees are binary decision trees with hyperplane splits based on canonical correlation components. Unlike axisaligned alternatives, the decision surfaces of CCFs are not restricted to the coordinate system of the input features and therefore more naturally r...

Bagging forms a committee of classijiers by bootstrap aggregation of training sets from a pool of training data. A simple alternative to bagging is to partition the data into disjoint subsets. Experiments on various datasets show that, given the same size partitions and bags, disjoint partitions result in betterperformance than bootstrap aggregates (bags). Many applications (e.g., protein struc...

10 Bagging forms a committee of classifiers by bootstrap aggregation of training sets from a pool of training data. A 11 simple alternative to bagging is to partition the data into disjoint subsets. Experiments with decision tree and neural 12 network classifiers on various datasets show that, given the same size partitions and bags, disjoint partitions result in 13 performance equivalent to, o...

We prove that there exist natural generalizations of the classical bootstrap percolation model on Z that have non-trivial critical probabilities, and moreover we characterize all homogeneous, local, monotone models with this property. Van Enter [28] (in the case d = r = 2) and Schonmann [25] (for all d > r > 2) proved that r-neighbour bootstrap percolation models have trivial critical probabili...

This paper describes a field trial for a recently developed ensemble called Additive Groves on KDD Cup’09 competition. Additive Groves were applied to three tasks provided at the competition using the ”small” data set. On one of the three tasks, appetency, we achieved the best result among participants who similarly worked with the small dataset only. Postcompetition analysis showed that less s...

The study analysed adoption of improved rice production technologies in Jere Local Government Area of Borno State, Nigeria. Descriptive statistics such as frequencies and percentages were used to analyse the data. The study revealed that 98.12% of the respondents were male, 57.5% were within 41-50 years and most (33.12%) of the respondents had adult education. The study revealed that majority (...

Ensemble learning has been shown to be very successful in data mining. However most work on ensemble learning concerns the task of classification. Little work has been done to construct ensembles that aim to improve ranking. In this paper, we propose an approach to re-construct new ensembles based on a given ensemble with the purpose to improve the ranking performance, which is crucial in many ...

A classification approach involving data tuning, followed by the use of a binary classification tree, augmented by bootstrap aggregation, yielded a ‘hit’ score of 110.

In a number of real-life applications, the user is interested in analyzing non vectorial data, for which kernels are useful tools that embed data into an (implicit) Euclidean space. However, when using such approaches with prototype-based methods, the computational time is related to the number of observations (because the prototypes are expressed as convex combinations of the original data). A...

Bagging is an ensemble method that relies on random resampling of a data set to construct models for the ensemble. When only statistics about the data are available, but no individual examples, the straightforward resampling procedure cannot be implemented. The question is then whether bagging can somehow be simulated. In this paper we propose a method that, instead of computing certain heurist...