Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Models obtained by decision tree induction techniques excel in being interpretable.Hover, they can be prone to overfitting, which results in a low predictive performance. Ensemble techniques are able to achieve a higher accuracy. Hover, this comes at a cost of losing interpretability of the resulting model. This makes ensemble techniques impractical in applications where decision support, instead of decision making, is crucial. #R##N#To bridge this gap,  present the GENESIM algorithm that transforms an ensemble of decision trees to a single decision tree with an enhanced predictive performance by using a genetic algorithm.  compared GENESIM to prevalent decision tree induction and ensemble techniques using tlve publicly available data sets. The results show that GENESIM achieves a better predictive performance on most of these data sets than decision tree induction techniques and a predictive performance in the same order of magnitude as the ensemble techniques. Moreover, the resulting model of GENESIM has a very low complexity, making it very interpretable, in contrast to ensemble techniques.
['Gilles Vandewiele', 'Olivier Janssens', 'Femke Ongenae', 'Filip De Turck', 'Sofie Van Hoecke']
I am writing a research paper on a idea GENESIM: genetic extraction of a single, interpretable model
[{"from": "human", "value": "I am writing a research paper on a idea GENESIM: genetic extraction of a single, interpretable model"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Models obtained by decision tree induction techniques excel in being interpretable.Hover, they can be prone to overfitting, which results in a low predictive performance. Ensemble techniques are able to achieve a higher accuracy. Hover, this comes at a cost of losing interpretability of the resulting model. This makes ensemble techniques impractical in applications where decision support, instead of decision making, is crucial. #R##N#To bridge this gap,  present the GENESIM algorithm that transforms an ensemble of decision trees to a single decision tree with an enhanced predictive performance by using a genetic algorithm.  compared GENESIM to prevalent decision tree induction and ensemble techniques using tlve publicly available data sets. The results show that GENESIM achieves a better predictive performance on most of these data sets than decision tree induction techniques and a predictive performance in the same order of magnitude as the ensemble techniques. Moreover, the resulting model of GENESIM has a very low complexity, making it very interpretable, in contrast to ensemble techniques."}]