Design of improved BACO high dimensional data dimension reduction model for data mining applications
With the deepening of medical and health information construction,medical data is on the rise both in type and scale.These massive data are often high-dimensional,so this study designed an improved binary ant colony optimization algorithm model for the problem of dimensionality reduction of high-dimensional data.Firstly,examination of the BACO-based feature selection process is being conducted,and then the feature weight of BACO algorithm is improved to reduce the dimension of high dimensional data.The performance of the improved BACO algorithm is tested.When the maximum number of iterations reaches about 50,the performance of the algorithm tends to be stable,and the performance is better than that of similar algorithms BACO and LCBBACO.A time cost comparison experiment was conducted on the high dimensional data reduction model based on RBFACO algorithm and BACO algo-rithm.Compared with the BACO high dimensional data reduction model on the Chronic Kidney Disease dataset,RBFACO reduced the time cost by about 62％.The above experimental results show that an improved binary ant colony optimization algorithm model is proposed,which can maintain the validity of data structure and improve the performance and efficiency of data mining while reducing the data dimension.
data miningBACOhigh dimensiondata dimensionality reduction modelRBFACO
万方数据
维普
