Earticle

The uncertain data as an important aspect of data mining, has received considerable attention, due to its importance in many applications, but little study has been paid to the cost-sensitive classification on uncertain data, so this paper proposes the dynamic cost-sensitive Naive Bayes classification for mining uncertain data (DCSUNB). Firstly, we apply the probability density to dispose uncertain discrete and continuous attributes, and give the cost-sensitive Naive Bayes classifier. Secondly, we propose the construction process of dynamic cost, and give the evaluation method for finding the optimal cost and the cost-sensitive classification with sequential test strategy. At last, the dynamic cost-sensitive Naive Bayes algorithm for uncertain data is structured, which searches the misclassification and test cost spaces to find the optimal cost. By comparing to the other cost-sensitive classification algorithms for uncertain data, the experiments on UCI Datasets show that DCSUNB can improve the classification performance, and reduce effectively the total cost.

The existing classifiers for uncertain data don’t consider the dynamic cost, so this paper proposes the classification approach of the dynamic cost-sensitive decision tree for uncertain data based on the genetic algorithm (GDCDTU) , which overcomes the limitations of the stationary cost, and searches automatically the suitable cost space of every sub datasets. Firstly, this paper gives the dynamic cost- sensitive learning thought, and disposes the continuous and discrete attributes for uncertain data by the probabilistic cardinality. Secondly, we give the selection methods for the splitting attributes and the construction process for cost-sensitive decision tree, and the interval number for describing dynamic cost is coded by its centre and radius. At last, the dynamic cost-sensitive decision tree for uncertain data is structured, which uses the genetic algorithm as the optimal misclassification cost searching way, and the optimum cost is got by the hybridization, the mutation, the selection. The experiments using both artificial and real data sets show that, compared to the other decision tree classification algorithms for uncertain data, GDCDTU has higher classification accuracy and performance, and the total expenditure is lower.

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.

The existing fussy clustering algorithms for uncertain data don’t consider the dynamic cost and the treatment effect is lower, so this paper proposes the dynamic cost-sensitive fussy clustering approach for uncertain data based on the genetic algorithm (GADCSFA). Firstly, this paper gives the definition of dynamic cost and adjacent interval, and the uncertain attributes are disposed as the interval number. Secondly, we give the method of fuzzy c-means clustering based on the interval data, and the interval numbers of fussy clustering solution and cost space are coded by its centre and radius. At last, the dynamic fussy clustering approach for uncertain data based on the genetic algorithm is structured, which uses the genetic algorithm to search the optimal clustering centre and cost by the hybridization, the mutation and selection. The experiments show that, compared to the other fussy clustering algorithm for uncertain data, GADCSFA has higher classification accuracy and performance, and the total expenditure is lower.

In order to lower the classification cost and improve the performance of the classifier, this paper proposes the approach of the dynamic cost-sensitive ensemble classification based on extreme learning machine for imbalanced massive data streams (DCECIMDS). Firstly, this paper gives the method of concept drifts detection by extracting the attributive characters of imbalanced massive data streams. If the change of attributive characters exceeds threshold value, the concept drift occurs. Secondly, we give Cost-sensitive extreme learning machine algorithm, and the optimal cost function is defined by the dynamic cost matrix. Build the cost-sensitive classifiers model for imbalanced massive data streams under MapReduce, and the data streams are processed in parallel. At last, the weighted cost-sensitive ensemble classifier is constructed, and the dynamic cost-sensitive ensemble classification based on extreme learning machine classification is given. The experiments demonstrate that the proposed ensemble classifier under the MapReduce framework can reduce the average misclassification cost and can make the classification results more reliable. DCECIMDS has high performance by comparing to the other classification algorithms for imbalanced data streams and can effectively deal with the concept drift.

In order to improve the performance of Support Vector Machine (SVM) classifier for imbalanced data, this paper proposes dynamic cost-sensitive SVM classifier based on chaos particle swarm optimization (CPDC_SVM). Firstly, this paper introduces dynamic cost-sensitive thought to SVM classifier, and gives the method for structuring dynamic cost and cost-sensitive SVM model. Secondly, we propose the evaluation methodology performance for classifier, and adopts decimal base to code the particles. At last, chaos thought is introduced in particle swarm optimization algorithm, and the Algorithm of the dynamic cost-sensitive SVM classifier is given, which improves convergent speed and accuracy of particle swarm optimization, and can optimize dynamic cost-sensitive SVM well, so CPDC_SVM adds effectively the convergence speed and accuracy for the particle swarm optimization algorithm. Experimental results show CPDC_SVM has higher precision than traditional SVM classifier, and dynamic cost and chaos particle swarm optimization can improve the performance for classifier.