BACKGROUND Gene expression data classification is a challenging task due to the large dimensionality and very small number of samples. Decision tree is one of the popular machine learning approaches to address such classification problems. However, the existing decision tree algorithms use a single gene feature at each node to split the data into its child nodes and hence might suffer from poor...

The Objective (Target) of this project was to prescribe a pre-screen test mailing program using a combination of a “Risk Score” and Specific Credit Bureau Attributes (CBA). CBA variables chosen were predictive of accounts that generate revenue in the Top 50% of the Company’s customer portfolio, have acceptable delinquency levels and have a “Risk Score” less than what is normally acceptable. A r...

This paper suggests a decision tree based approach for flexible job shop scheduling with multiple process plans, i.e. each job can be processed through alternative operations, each of which can be processed on alternative machines. The main decision variables are: (a) selecting operation/machine pair; and (b) sequencing the jobs assigned to each machine. As an extension of the priority scheduli...

Parallel decision tree learning is an effective and efficient approach to scaling the decision tree to large data mining application. Aiming at large scale decision tree learning, we present a novel parallel decision tree learning algorithm in MapReduce framework, called PDTSSE (Parallel Decision Tree via Sampling Splitting points with Estimation). We first propose an estimation method for samp...

Feature selection study is gaining importance due to its contribution to save classification cost in terms of time and computation load. In search of essential features, one of the methods to search the features is via the decision tree. Decision tree act as an intermediate feature space inducer in order to choose essential features. In decision tree-based feature selection, some studies used d...

Decision tree based schemes are widely used in designing high-speed packet classification algorithms. The primary objective is to construct a decision tree with minimal storage and searching time complexity. In this paper, we proposed a novel decision tree packet classification algorithm based on Efficient Multiple Bit Selection (EMBS). In the proposed algorithm, prefix fields are transformed t...