Evolutionary model trees for handling continuous classes in machine learning

Automatic design of decision-tree algorithms with evolutionary algorithms

This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. The automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. The proposed hyper-heuristic evolutionary algorithm, HEAD-DT, is extensively tested using 20 public UCI datasets and 10 microarray gene expression datasets. The algorithms automatically designed by HEAD-DT are compared with traditional decision-tree induction algorithms, such as C4.5 and CART. Experimental results show that HEAD-DT is capable of generating algorithms which are significantly more accurate than C4.5 and CART.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

Background

This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance.

Results

The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application.

Conclusions

We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

Clus-DTI: improving decision-tree classification with a clustering-based decision-tree induction algorithm

Decision-tree induction is a well-known technique for assigning objects to categories in a white-box fashion. Most decision-tree induction algorithms rely on a sub-optimal greedy top-down recursive strategy for growing the tree. Even though such a strategy has been quite successful in many problems, it presents several deficiencies. For instance, there are cases in which the hyper-rectangular surfaces generated by these algorithms can only fit the input space after several sequential partitions, which results in a large and incomprehensible tree. In this paper, we propose a new decision-tree induction algorithm based on clustering named Clus-DTI. Our intention is to investigate how clustering data as a part of the induction process affects the accuracy and complexity of the generated models. Our performance analysis is not based solely on the straightforward comparison of our proposed algorithm to baseline classifiers. We also perform a data-dependency analysis in order to identify scenarios in which Clus-DTI is a more suitable option for inducing decision trees.