Decision tree learning is one of the most popular classification techniques. However, by its nature it is a greedy approach to finding a classification hypothesis that optimizes some information-based criterion. It is very fast but may lead to finding suboptimal classification hypotheses. Moreover, in spite of decision trees being easily interpretable, ensembles of trees (random forests and gra...

To estimate the type of fertilizer based on soil minerals using voting classifier. For forecasting accuracy %, a Voting Classifier with sample size 10 and Decision Tree was iterated at various times. A supervised learning algorithm is Tree. It constructs “forest” an array decision trees, typically trained to use “bagging” method. Novel Classification predictive model that learns from several mo...

BACKGROUND Classification and regression tree analysis involves the creation of a decision tree by recursive partitioning of a dataset into more homogeneous subgroups. Thus far, there is scarce literature on using this technique to create clinical prediction tools for aneurysmal subarachnoid hemorrhage (SAH). METHODS The classification and regression tree analysis technique was applied to the...

Decision tree classification procedures have been largely overlooked in remote sensing applications. In this paper we compare the classification performance of three types of decision trees across three different data sets. The classifiers that are considered include a univariate decision tree, multivariate decision tree, and a hybrid decision tree. Results from an n-fold cross-validation proce...

Integration of data mining in database systems is an open topic of research. The DBMS's power of dealing with lots of data and maintaining data integrity adds to the motivation of integrating it with data mining. We propose a method to integrate decision tree classification to do the required precomputations and store it in database objects for later use. These pre-computed values get updated w...

This paper highlights the significance of classification in data mining and knowledge discovery. In this paper we investigate the performance of various data mining classification algorithms viz. Rnd Tree, Quinlan decision tree algorithm (C4.5), KNearest Neighbor algorithm etc., on a large dataset from the „Wisconsin Breast tissue dataset‟ (derived from the UCI Machine Learning Repository) that...

Multi-class support vector machines with binary tree architecture (SVM-BTA) have the fastest decision-making speed in the existing multi-class SVMs. But SVM-BTA usually has bad classification capability. According to internal characteristics of feature samples, this paper uses resemblance coefficient method to construct automatically binary tree to incorporate multiple binary SVMs. The multi-cl...

Abstract— For multi-class classification with Support Vector Machines (SVMs) a binary decision tree architecture is proposed for computational efficiency. The proposed SVMbased binary tree takes advantage of both the efficient computation of the tree architecture and the high classification accuracy of SVMs. A modified Self-Organizing Map (SOM), KSOM (Kernel-based SOM), is introduced to convert...