Treemodels are valuable tools for predictivemodeling and datamining. Traditional tree-growingmethodologies such as CART are known to suffer from problems including greediness, instability, and bias in split rule selection. Alternative tree methods, including Bayesian CART (Chipman et al., 1998; Denison et al., 1998), random forests (Breiman, 2001a), bootstrap bumping (Tibshirani and Knight, 199...

In recent years, stream data have become an immensely growing area of research for the database, computer science and data mining communities. Stream data is an ordered sequence of instances. In many applications of data stream mining data can be read only once or a small number of times using limited computing and storage capabilities. Some of the issues occurred in classifying stream data tha...

The classification-tree method developed by Grochtmann and Grimm facilitates the identification of test cases from functional specifications via the construction of classification trees. Their method has been enhanced by Chen and Poon through the classification-tree construction and restructuring methodologies. We find, however, that the restructuring algorithm by Chen and Poon is applicable on...

MOTIVATION Glycans are covalent assemblies of sugar that play crucial roles in many cellular processes. Recently, comprehensive data about the structure and function of glycans have been accumulated, therefore the need for methods and algorithms to analyze these data is growing fast. RESULTS This article presents novel methods for classifying glycans and detecting discriminative glycan motifs...

The most efficient speciation methods suffer from a quite high complexity from O(n c(n)) to O(n2), where c(n) is a factor that can be proportional to n, the population size. In this paper, a speciation method based on a classification tree is presented, having a complexity of O(n logn). The population is considered as a set of attribute vectors to train the classification tree. The splitting me...

Classification is a data mining (DM) technique used to predict or forecast the unknown information using the historical data. There are many classification techniques. ID3 is a very popular tree based classification algorithm for a categorical data which does not support continuous data. Attribute selection process plays major role in building a classification tree model. Attribute Selection in...

A classification or regression tree is a prediction model that can be represented as a decision tree. This article discusses the C4.5, CART, CRUISE, GUIDE, and QUEST methods in terms of their algorithms, features, properties, and performance.

Decision trees, either classification or regression trees, are especially attractive type of models for three main reasons. First, they have an intuitive representation, the resulting model is easy to understand and assimilate by humans [BFOS84]. Second, the decision trees are nonparametric models, no intervention being required from the user, and thus they are very suited for exploratory knowl...