Ensemble of Example-Dependent Cost-Sensitive Decision Trees

Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples and not only within classes. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. In previous works, some methods that take into account the financial costs into the training of different algorithms have been proposed, with the example-dependent cost-sensitive decision tree algorithm being the one that gives the highest savings. In this paper we propose a new framework of ensembles of example-dependent cost-sensitive decision-trees. The framework consists in creating different example-dependent cost-sensitive decision trees on random subsamples of the training set, and then combining them using three different combination approaches. Moreover, we propose two new cost-sensitive combination approaches; cost-sensitive weighted voting and cost-sensitive stacking, the latter being based on the cost-sensitive logistic regression method. Finally, using five different databases, from four real-world applications: credit card fraud detection, churn modeling, credit scoring and direct marketing, we evaluate the proposed method against state-of-the-art example-dependent cost-sensitive techniques, namely, cost-proportionate sampling, Bayes minimum risk and cost-sensitive decision trees. The results show that the proposed algorithms have better results for all databases, in the sense of higher savings.Comment: 13 pages, 6 figures, Submitted for possible publicatio

CSNL: A cost-sensitive non-linear decision tree algorithm

This article presents a new decision tree learning algorithm called CSNL that induces Cost-Sensitive Non-Linear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision nodes and utilizes discriminant analysis to construct nonlinear decision trees that take account of costs of misclassification. The performance of the algorithm is evaluated by applying it to seventeen datasets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the datasets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using nonlinear decision nodes. The performance of the algorithm is evaluated by applying it to seventeen data sets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the data sets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using non-linear decision nodes

Model-based analysis of the potential of macroinvertebrates as indicators for microbial pathogens in rivers

The quality of water prior to its use for drinking, farming or recreational purposes must comply with several physicochemical and microbiological standards to safeguard society and the environment. In order to satisfy these standards, expensive analyses and highly trained personnel in laboratories are required. Whereas macroinvertebrates have been used as ecological indicators to review the health of aquatic ecosystems. In this research, the relationship between microbial pathogens and macrobenthic invertebrate taxa was examined in the Machangara River located in the southern Andes of Ecuador, in which 33 sites, according to their land use, were chosen to collect physicochemical, microbiological and biological parameters. Decision tree models (DTMs) were used to generate rules that link the presence and abundance of some benthic families to microbial pathogen standards. The aforementioned DTMs provide an indirect, approximate, and quick way of checking the fulfillment of Ecuadorian regulations for water use related to microbial pathogens. The models built and optimized with the WEKA package, were evaluated based on both statistical and ecological criteria to make them as clear and simple as possible. As a result, two different and reliable models were obtained, which could be used as proxy indicators in a preliminary assessment of pollution of microbial pathogens in rivers. The DTMs can be easily applied by staff with minimal training in the identification of the sensitive taxa selected by the models. The presence of selected macroinvertebrate taxa in conjunction with the decision trees can be used as a screening tool to evaluate sites that require additional follow up analyses to confirm whether microbial water quality standards are met