In this study, a multistage evolutionary programming (EP) based support vector machine (SVM) ensemble model is proposed for designing a corporate bankruptcy prediction system to discriminate healthful firms from bad ones. In the proposed model, a bagging sampling technique is first used to generate different training sets. Based on the different training sets, some different SVM models with dif...

We experimentally evaluate bagging and six other randomization-based approaches to creating an ensemble of decision-tree classifiers. Bagging uses randomization to create multiple training sets. Other approaches, such as Randomized C4.5 apply randomization in selecting a test at a given node of a tree. Then there are approaches, such as random forests and random subspaces, that apply randomizat...

Bagging an estimator approximately doubles its bias through the impact of bagging on quadratic terms in expansions of the estimator. This difficulty can be alleviated by bagging a suitably bias-corrected estimator, however. In these and other circumstances, what is the overall impact of bagging and/or bias correction, and how can it be characterised? We answer these questions in the case of gen...

Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naı̈ve Bayes classifiers at the leaves. MOA supports bi-directi...

Classifier ensembles is an active area of research within the machine learning community. One of the most successful techniques is bagging, where an algorithm (typically a decision tree inducer) is applied over several different training sets, obtained applying sampling with replacement to the original database. In this paper we define a framework where sampling with and without replacement can...

Constrained Laplacian Score (CLS) is a recently proposed method for semi-supervised feature selection. It presented an outperforming performance comparing to other methods in the state of the art. This is because CLS exploits both unsupervised and supervised parts of data for selecting the most relevant features. However, the choice of the little supervision information (represented by pairwise...

A two stage burned scar detection approach is applied to produce a burned scar map for Mediterranean Europe using IRS-AWiFS imagery acquired at the end of the 2009 fire season. The first stage identified burned scar seeds based on a learning algorithm (Artificial Neural Network) coupled with a bootstrap aggregation process. The second stage implemented a region growing process to extend the are...

The present work analyzes different randomized methods to learn Markov tree mixtures for density estimation in very high-dimensional discrete spaces (very large number n of discrete variables) when the sample size (N) is very small compared to n. Several subquadratic relaxations of the Chow-Liu algorithm are proposed, weakening its search procedure. We first study näıve randomizations and then ...

Churn rate refers to the proportion of contractual customers who leave a supplier during a given time period. This phenomenon is very common in highly competitive markets such as telecommunications industry. In a statistical setting, churn can be considered as an outcome of some characteristics and past behavior of customers. In this paper, churn prediction is performed considering a real datas...