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.

Complexity scalable intra-prediction mode decision algorithm for mobile video applications

Abstract: © The Institution of Engineering and Technology 2014. The full search scheme employed in H.264/AVC significantly improves the coding performance, but it also introduces a very high computational complexity which limits the applications in resource-constrained mobile devices. In this study, the authors firstly present a discretisation total variation and orientation gradient-based hierarchical intra-prediction mode decision method for mobile video applications. By shrinking the candidate mode set in the rate-distortion optimisation (RDO) process, the proposed algorithm reduces the computational complexity and power consumption of the encoder. Furthermore, they extend the hierarchical algorithm to a complexity scalable version in which the coding complexity is measured on five levels by reserving various numbers of modes for RDO. Experimental results demonstrate that the proposed mode decision algorithm reduces the coding complexity significantly with negligible performance degradation and the proposed complexity scalable algorithm is effective and efficient for mobile video application.

Multi-layer differential privacy embedded decision tree model-based privacy risk control method

Abstract: The invention provides a multi-layer differential privacy embedded decision tree model-based privacy risk control method. The method includes the following steps that: initialization is carried out; a differential privacy technology is embedded into a multi-layer decision tree model; and a multi-layer decision tree is obtained. According to the method of the invention, a multi-layer embedding mode is adopted to embed the differential privacy technology into the decision tree model. Compared with the prior art, the decision tree model can be under the protection of differential privacies, and the prediction accuracy of the model is greatly improved.

The Complexity of a Probabilistic Approach to Deal with Missing Values in a Decision Tree

Abstract: We describe the complexity of an approach to fill missing values in decision trees during classification. This approach is derived from the ordered attribute trees method which builds a decision tree for each attribute and uses these trees to fill the missing attribute values. Both our approach and theirs are based on the Mutual Information between the attributes and the class. Our method takes into account the dependence between attributes by using Mutual Information. The result of the classification process is a probability distribution instead of a single class. In this paper, we explain our classification algorithm. We then calculate the complexity of constructing the attribute trees and the complexity of classifying a new instance with missing values using our classification algorithm.

A text based Decision Tree model for stock market forecasting

Abstract: Decision Tree is a well established data mining techniques equipped with several algorithms to suit both linear and non linear data set for forecasting future trend. In this paper an attempt has been made to implement text based decision tree having all discrete input variables rather than a numerical decision tree where at least one variable is need to be discrete. The available historical data and other technical indicators calculated over the numerical data set of BSE sensex and NSE nifty has been converted and normalized to textual form by certain rules and decision trees are differently constructed for BSE sensex and NSE nifty with application of C4.5 algorithm and compared with the usual decision tree generated directly by applying numerical variables for the same period. The empirical study proves the better efficacy of the proposed model by outperforming the usual decision tree.