Decision tree model

About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.

BehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model

Abstract: This paper formulates the problem of building a context-aware predictive model based on user diverse behavioral activities with smartphones. In the area of machine learning and data science, a tree-like model as that of decision tree is considered as one of the most popular classification techniques, which can be used to build a data-driven predictive model. The traditional decision tree model typically creates a number of leaf nodes as decision nodes that represent context-specific rigid decisions, and consequently may cause overfitting problem in behavior modeling. However, in many practical scenarios within the context-aware environment, the generalized outcomes could play an important role to effectively capture user behavior. In this paper, we propose a behavioral decision tree, “BehavDT” context-aware model that takes into account user behavior-oriented generalization according to individual preference level. The BehavDT model outputs not only the generalized decisions but also the context-specific decisions in relevant exceptional cases. The effectiveness of our BehavDT model is studied by conducting experiments on individual user real smartphone datasets. Our experimental results show that the proposed BehavDT context-aware model is more effective when compared with the traditional machine learning approaches, in predicting user diverse behaviors considering multi-dimensional contexts.

On the communication complexity of graph properties

Abstract: We prove t(n log n) bounds for the deterministic 2-way communication complexity of the graph properties CONNECTIVITY, s-t-CONNECTIVITY and BIPARTITENESS (for arbitrary partitions of the variables into two sets of equal size). The proofs are based on combinatorial results of Dowling-Wilson and Lovasz-Saks about partition matrices using the Mobius function, and the Regularity Lemma of Szemeredi. The bounds imply improved lower bounds for the VLSI complexity of these decision problems and sharp bounds for a generalized decision tree model which is related to the notion of evasiveness.

Cost Complexity-Based Pruning of Ensemble Classifiers

Abstract: In this paper we study methods that combine multiple classification models learned over separate data sets. Numerous studies posit that such approaches provide the means to efficiently scale learning to large data sets, while also boosting the accuracy of individual classifiers. These gains, however, come at the expense of an increased demand for run-time system resources. The final ensemble meta-classifier may consist of a large collection of base classifiers that require increased memory resources while also slowing down classification throughput. Here, we describe an algorithm for pruning (i.e., discarding a subset of the available base classifiers) the ensemble meta-classifier as a means to reduce its size while preserving its accuracy and we present a technique for measuring the trade-off between predictive performance and available run-time system resources. The algorithm is independent of the method used initially when computing the meta-classifier. It is based on decision tree pruning methods and relies on the mapping of an arbitrary ensemble meta-classifier to a decision tree model. Through an extensive empirical study on meta-classifiers computed over two real data sets, we illustrate our pruning algorithm to be a robust and competitive approach to discarding classification models without degrading the overall predictive performance of the smaller ensemble computed over those that remain after pruning.