52,056 research outputs found
Data Mining Criteria for Tree-Based Regression and Classification
This paper is concerned with the construction of regression and classification trees that are more adapted to data mining applications than conventional trees. To this end, we propose new splitting criteria for growing trees. Conventional splitting criteria attempt to perform well on both sides of a split by attempting a compromise in the quality of fit between the left and the right side. By contrast, we adopt a data mining point of view by proposing criteria that search for interesting subsets of the data, as opposed to modeling all of the data equally well. The new criteria do not split based on a compromise between the left and the right bucket; they effectively pick the more interesting bucket and ignore the other.As expected, the result is often a simpler characterization of interesting subsets of the data. Less expected is that the new criteria often yield whole trees that provide more interpretable data descriptions. Surprisingly, it is a flaw that works to their advantage: The new criteria have an increased tendency to accept splits near the boundaries of the predictor ranges. This so-called end-cut problem leads to the repeated peeling of small layers of data and results in very unbalanced but highly expressive and interpretable trees
Classic and Bayesian Tree-Based Methods
Tree-based methods are nonparametric techniques and machine-learning methods for data prediction and exploratory modeling. These models are one of valuable and powerful tools among data mining methods and can be used for predicting different types of outcome (dependent) variable: (e.g., quantitative, qualitative, and time until an event occurs (survival data)). Tree model is called classification tree/regression tree/survival tree based on the type of outcome variable. These methods have some advantages over against traditional statistical methods such as generalized linear models (GLMs), discriminant analysis, and survival analysis. Some of these advantages are: without requiring to determine assumptions about the functional form between outcome variable and predictor (independent) variables, invariant to monotone transformations of predictor variables, useful for dealing with nonlinear relationships and high-order interactions, deal with different types of predictor variable, ease of interpretation and understanding results without requiring to have statistical experience, robust to missing values, outliers, and multicollinearity. Several classic and Bayesian tree algorithms are proposed for classification and regression trees, and in this chapter, we provide a review of these algorithms and appropriate criteria for determining the predictive performance of them
Data mining as a tool for environmental scientists
Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous
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
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