Furthest-Pair-Based Decision Trees: Experimental Results on Big Data Classification

Big Data Classification Using Augmented Decision Trees

We present an algorithm for classification tasks on big data. Experiments conducted as part of this study indicate that the algorithm can be as accurate as ensemble methods such as random forests or gradient boosted trees. Unlike ensemble methods, the models produced by the algorithm can be easily interpreted. The algorithm is based on a divide and conquer strategy and consists of two steps. Th...

Component-based decision trees for classification

Typical data mining algorithms follow a so called “black-box” paradigm, where the logic is hidden from the user not to overburden him. We show that “white-box” algorithms constructed with reusable components design can have significant benefits for researchers, and end users as well. We developed a component-based algorithm design platform, and used it for “white-box” algorithm construction. Th...

Tackling Simpson's Paradox in Big Data using Classification & Regression Trees

This work is aimed at finding potential Simpson’s paradoxes in Big Data. Simpson’s paradox (SP) arises when choosing the level of data aggregation for causal inference. It describes the phenomenon where the direction of a cause on an effect is reversed when examining the aggregate vs. disaggregates of a sample or population. The practical decision making dilemma that SP raises is which level of...

Hierarchical Protein Classification based on Gene Ontology and Decision Trees

Proteins are the most important cell parts, therefore, knowing their exact function is of a great significance. However, the function of large amount of proteins is still unknown. In addition, today, biologists persist on hierarchical organization the living world, and thus in protein databases also. There are many protein classification algorithms proposed determining the protein function, but...

Decision Trees for Classification: a Review and Some New Results