With the recent financial crisis and European debt crisis, corporate bankruptcy prediction has become an increasingly important issue for financial institutions. Many statistical and intelligent methods have been proposed, however, there is no overall best method has been used in predicting corporate bankruptcy. Recent studies suggest ensemble learning methods may have potential applicability i...

Medium-scale land cover maps are traditionally created on the basis of a single cloud-free satellite scene, leaving information present in other scenes unused. Using 1309 field observations and 20 cloudand error-affected Landsat scenes covering Zanzibar Island, this study demonstrates that the use of multiple scenes can both allow complete coverage of the study area in the absence of cloud-free...

The standard framework of machine learning problems assumes that the available data is independent and identically distributed (i.i.d.). However, in some applications such as image classification, the training data are often collected from multiple sources and heterogeneous. Ensemble learning is a proven effective approach to heterogeneous data, which uses multiple classification models to capt...

Ensemble learning schemes such as AdaBoost and Bagging enhance the performance of a single classifier by combining predictions from multiple classifiers of the same type. The predictions from an ensemble of diverse classifiers can be combined in related ways, e.g. by voting or simply by selecting the best classifier via cross-validation a technique widely used in machine learning. However, sinc...

This paper presents an ensemble learning algorithm for liver tumour segmentation from a CT volume in the form of U-Boost and extends the loss functions to improve performance. Five segmentation algorithms trained by the ensemble learning algorithm with different loss functions are compared in terms of error rate and Jaccard Index between the extracted regions and true ones. key words: CT image,...

Recent advances of gradient temporal-difference methods allow to learn off-policy multiple value functions in parallel without sacrificing convergence guarantees or computational efficiency. This opens up new possibilities for sound ensemble techniques in reinforcement learning. In this work we propose learning an ensemble of policies related through potential-based shaping rewards. The ensembl...

Generic ensemble methods can achieve excellent learning performance, but are not good candidates for active learning because of their different design purposes. We investigate how to use diversity of the member classifiers of an ensemble for efficient active learning. We empirically show, using benchmark data sets, that (1) to achieve a good (stable) ensemble, the number of classifiers needed i...

Ensemble learning (process of combining multiple models into a single decision) is an effective tool for improving the classification performance of inductive models. While ideal for domains like bioinformatics with many challenging datasets, many ensemble methods, such as Bagging and Boosting, do not take into account the high-dimensionality (large number of features per instance) that is comm...

In this paper, we describe the participation of the SentiME++ system to the SemEval 2017 Task 4A “Sentiment Analysis in Twitter” that aims to classify whether English tweets are of positive, neutral or negative sentiment. SentiME++ is an ensemble approach to sentiment analysis that leverages stacked generalization to automatically combine the predictions of five state-of-the-art sentiment class...

We propose and evaluate alternative ensemble schemes for a new instance based learning classifier, the Randomised Sphere Cover (RSC) classifier. RSC fuses instances into spheres, then bases classification on distance to spheres rather than distance to instances. The randomised nature of RSC makes it ideal for use in ensembles. We propose two ensemble methods tailored to the RSC classifier; αβRS...