Classification Algorithm in Data Mining Based on Maximum Exponential Class Counts Technique

A new split attribute measure for decision tree node split during decision tree creation is proposed. The new split measure consists of the sum of class counts of distinct values of categorical attributes in the dataset. Larger counts induce larger partitions and smaller trees there by favors to the determination of the best spit attribute. The new split attribute measure is termed as maximum exponential class counts (MECC). Experiment results obtained over several UCI machine learning categorical datasets predominantly indicate that the decision tree models created based on the proposed MECC node split attribute technique provides better classification accuracy results and smaller trees in size than the decision trees created using popular gain ratio, normalized gain ratio and gini-index measures. The experimental results are mainly focused on performing and analyzing the results from the node splitting measures alone.

Uncertainty Analysis on Risk Assessment of Water Inrush in Karst Tunnels

An improved attribute recognition method is reviewed and discussed to evaluate the risk of water inrush in karst tunnels. Due to the complex geology and hydrogeology, the methodology discusses the uncertainties related to the evaluation index and attribute measure. The uncertainties can be described by probability distributions. The values of evaluation index and attribute measure were employed through random numbers generated by Monte Carlo simulations and an attribute measure belt was chosen instead of the linearity attribute measure function. Considering the uncertainties of evaluation index and attribute measure, the probability distributions of four risk grades are calculated using random numbers generated by Monte Carlo simulation. According to the probability distribution, the risk level can be analyzed under different confidence coefficients. The method improvement is more accurate and feasible compared with the results derived from the attribute recognition model. Finally, the improved attribute recognition method was applied and verified in Longmenshan tunnel in China.

Development of an Evaluation Model of Freeway Traffic Safety Facilities System

This study developed an evaluation model of freeway traffic safety facilities system. Firstly, an evaluation system of freeway traffic safety facility was proposed. Secondly, an evaluation model was proposed based on attribute recognition theory. And the evaluation result was identified according to the attribute measure value of single index and the comprehensive attribute measure value of multiple indexes as well as the confidence criterion. Thirdly, the weight of each indicator was decided by variation coefficient. Finally, A case of TAI-GAN freeway (K1+242~K3+259 segment) was conducted to verify the feasibility and effectiveness of the model.