title: Decision Trees for Continuous Data and Conditional Mutual Information as a Criterion for Splitting Instances.
abstract: Decision trees are renowned in the computational chemistry and machine learning communities for their interpretability. Their capacity and usage are somewhat limited by the fact that they normally work on categorical data. Improvements to known decision tree algorithms are usually carried out by increasing and tweaking parameters, as well as the post-processing of the class assignment. In this work we attempted to tackle both these issues. Firstly, conditional mutual information was used as the criterion for selecting the attribute on which to split instances. The algorithm performance was compared with the results of C4.5 (WEKA's J48) using default parameters and no restrictions. Two datasets were used for this purpose, DrugBank compounds for HRH1 binding prediction and Traditional Chinese Medicine formulation predicted bioactivities for therapeutic class annotation. Secondly, an automated binning method for continuous data was evaluated, namely Scott's normal reference rule, in order to allow any decision tree to easily handle continuous data. This was applied to all approved drugs in DrugBank for predicting the RDKit SLogP property, using the remaining RDKit physicochemical attributes as input.
authors.forename: Georgios
authors.initials: G
authors.lastname: Drakakis
authors.forename: Saadiq
authors.initials: S
authors.lastname: Moledina
authors.forename: Charalampos
authors.initials: C
authors.lastname: Chomenidis
authors.forename: Philip
authors.initials: P
authors.lastname: Doganis
authors.affiliation: School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou, 15780 Zografou, Greece. hsarimv@central.ntua.gr.
authors.forename: Haralambos
authors.initials: H
authors.lastname: Sarimveis
mesh_terms: D000465:Algorithms; D066264:Datasets as Topic / Q000145:classification / Q000592:standards; D003663:Decision Trees; D008516:Medicine, Chinese Traditional / Q000379:methods; D004364:Pharmaceutical Preparations / Q000145:classification
chemical_list: D004364:Pharmaceutical Preparations