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A common problem when using complicated models for prediction and classification is that the complexity of the model entails that it is hard, or impossible, to interpret. For some scenarios this might not be a limitation, since the priority is the accuracy of the model. In other situations the limitations might be severe, since additional aspects are(More)
Some data mining problems require predictive models to be not only accurate but also comprehensible. Comprehensibility enables human inspection and understanding of the model, making it possible to trace why individual predictions are made. Since most high-accuracy techniques produce opaque models, accuracy is, in practice, regularly sacrificed for(More)
Most highly accurate predictive modeling techniques produce opaque models. When comprehensible models are required, rule extraction is sometimes used to generate a transparent model, based on the opaque. Naturally, the extracted model should be as similar as possible to the opaque. This criterion, called fidelity, is therefore a key part of the optimization(More)
IGF-I treatment has been shown to enhance cell genesis in the brains of adult GH- and IGF-I-deficient rodents; however, the influence of GH therapy remains poorly understood. The present study investigated the effects of peripheral recombinant bovine GH (bGH) on cellular proliferation and survival in the neurogenic regions (subventricular zone (SVZ), and(More)
Both theory and a wealth of empirical studies have established that ensembles are more accurate than single predictive models. Unfortunately, the problem of how to maximize ensemble accuracy is, especially for classification, far from solved. In essence, the key problem is to find a suitable criterion, typically based on training or selection set(More)
This paper addresses the important issue of the tradeoff between accuracy and comprehensibility in data mining. The paper presents results which show that it is, to some extent, possible to bridge this gap. A method for rule extraction from opaque models (Genetic Rule EXtraction – G-REX) is used to show the effects on accuracy when forcing the creation of(More)
—In conformal prediction, predictive models output sets of predictions with a bound on the error rate. In classification , this translates to that the probability of excluding the correct class is lower than a predefined significance level, in the long run. Since the error rate is guaranteed, the most important criterion for conformal predictors is(More)
This paper presents G-REX, a versatile data mining framework based on genetic programming. What differs G-REX from other GP frameworks is that it doesn't strive to be a general purpose framework. This allows G-REX to include more functionality specific to data mining like preprocessing, evaluation- and optimization methods, but also a multitude of(More)