Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data. BMC Bioinformatics, 13.Barros RC, Winck AT, Machado KS, Basgalupp MP, Carvalho AC, Ruiz DD, Norberto de Souza O: Automatic design of decision-tree induction algorithms tailored to flexible-receptor ...
Decision-tree induction algorithms have been successfully used in drug-design related applications[16–19]. One of the main advantages of these algorithms when compared to other machine learning techniques (e.g., SVMs and Neural Networks) is that decision trees are simple to understand, interpret ...
Cite this article Gomes Mantovani, R., Horváth, T., Rossi, A.L.D.et al.Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms.Data Min Knowl Disc38, 1364–1416 (2024). https://doi.org/10.1007/s10618-024-01002-5 Download citation Re...
The learning and classification steps of decision tree induction are simple and fast. In general, decision tree classifiers have good accuracy. However, successful use may depend on the data at hand. Decision tree induction algorithms have been used for classification in many application areas such ...
Decision Tree Induction in Data Mining - Explore the concept of Decision Tree Induction in Data Mining, its algorithms, applications, and advantages for effective data analysis.
Decision tree induction algorithms such as C4.5 have incorporated in their learning phase an automatic feature selection strategy, while some other statistical classification algorithms require the feature subset to be selected in a preprocessing phase. It is well known that correlated and irrelevant ...
ting. The distributed decision tree induction algorithms have been implemented as part of INDUS, an agent-based system for data-driven knowledge acquisition from heterogeneous, distributed, autonomous data sources. 1 Introduction Recent advances in computing, communications, and digital storage technolo- ...
Classical decision tree induction algorithms, such as ID3 [12] and C4.5 [13], construct a decision tree incrementally, given a population of example instances labeled by known classes. The algorithms start with the entire population and select an attribute that would split the population into diff...
The standard Decision Tree induction algorithms have the limitation that the selection of the splitting test is based on fewer and fewer instances as the tree grows downwards. Therefore, the splitting tests that are near the bottom of the tree are often poorly chosen because they are based on ...
Existing decision tree induction algorithms, including the presented one using McDiarmid’s Inequality, work only with vector inputs. Therefore, it is not possible to apply them directly on tensor data without conducting vectorization. In order to alleviate this drawback and extend the applicability ...