Ulf Johansson

Learn More
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)
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)
We have previously shown that recombinant human (rh) IGF-I induces cell proliferation and neurogenesis in the hippocampus of hypophysectomized rats. In the current investigation, we determined the effects of rhIGF-I on proliferation and differentiation in the cerebral cortex. Adult hypophysectomized rats were injected with bromodeoxyuridine (BrdU) to label(More)
When performing predictive modeling, the key criterion is always accuracy. With this in mind, complex techniques like neural networks or ensembles are normally used, resulting in opaque models impossible to interpret. When models need to be comprehensible, accuracy is often sacrificed by using simpler techniques directly producing transparent models; a(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)
When designing ensembles, it is almost an axiom that the base classifiers must be diverse in order for the ensemble to generalize well. Unfortunately, there is no clear definition of the key term diversity, leading to several diversity measures and many, more or less ad hoc, methods for diversity creation in ensembles. In addition, no specific diversity(More)
Rule extraction is a technique aimed at transforming highly accurate opaque models like neural networks into comprehensible models without losing accuracy. G-REX is a rule extraction technique based on Genetic Programming that previously has performed well in several studies. This study has two objectives, to evaluate two new fitness functions for G-REX and(More)