원문정보
초록
영어
Due to its importance in many applications, the incomplete data mining has received increasing attention in recent years, but there has been little study of the cost-sensitive classification on incomplete data. Therefore this paper proposes the dynamic cost-sensitive extreme learning machine for classification of incomplete data based on the deep imputation network (DCELMIDC). Firstly, we propose an approach for incomplete data imputation based on the deep imputation network model, and offer the cost-sensitive extreme learning machine. Secondly, this paper introduces dynamic misclassification and test cost, and gives the chromosome coding and an evaluation method of the optimal cost. At last, on the basis of the genetic algorithm, the dynamic cost-sensitive extreme learning machine classification algorithm for mining incomplete data is given, which can search the optimal misclassification and test cost in cost spaces. The experiment results show that DCELMIDC is effective and feasible for classification of incomplete data, and can reduce the total cost.
목차
1. Introduction
2. Data Imputation for Incomplete Data Based on the Deep Imputation Network Model
2.1. Fill-in Automatic Code Machine
2.2. Imputation Algorithm for Uncertain Data Based on the Deep Imputation Network
3. Cost-Sensitive Extreme Learning Machine
3.1. Extreme Learning Machine
3.2. Cost-Sensitive Extreme Learning Machine
4. The Proposed Scheme
4.1. Dynamic Misclassification and Test Cost
4.2. Evaluation Method for the Optimal Cost
4.3. Chromosome Coding
4.4. Dynamic Cost-Sensitive Extreme Learning Machine for Mining Incomplete DataBase on the Genetic Algorithm
5. Simulation Experiment
5.1. Data Processing and Background Parameters
5.2. Result of Experiments
6. Conclusion
Acknowledgement
References