A Survey on Issues of Decision Tree and Non-Decision Tree Algorithms
C. Kishor Kumar Reddy, Vijaya Babu · International journal of artificial intelligence and applications for smart devices · 2016
Decision tree and non-decision tree approaches are effectively used in many diverse areas such as speech recognition, radar signal classification, satellite signal classification, medical diagnosis, remote sensing, expert systems, and weather forecasting and so on. Even though classification has been studied widely in the past, many of the algorithms are designed only for memory-resident data, thus limiting their suitability for data mining large data sets. The volume of data in databases is growing to quite large sizes, both in the number of attributes, instances and class labels. Decision tree learning from a very huge set of records in a database is quite complex task and is usually a very slow process, which is often beyond the capabilities of existing computers. This paper is an attempt to summarize the proposed approaches, tools etc. for decision tree learning with emphasis on optimization of constructed trees and handling large datasets. Further, we also discussed and summarized various non-decision tree approaches like Neural Networks, Support Vector Machines, and Naive Bayes and so on.