Research On Bayes Method Based Data Classification

Data mining is the product of the development of information technology,which is a complex process extracting the implicated and valuable patterns,knowledge and rules from a large scale dataset.Data classification is one of the important topics in the field of data mining. Bayes Method,which bases on solid mathematics theories and comprehensively considers the prior information and data sample information,is being widely studied and used in recent years.In this paper,data classification algorithm based on Bayes Method is mainly studied,and the research work consists of two parts as follows:(1)Aiming at the problem of fault-tolerant event region detection in wireless sensor networks(WSN),this paper propose the idea of distributed weighted classification fault-tolerant detection:considering the range of neighborhood's neighborhood,we first use information exchange between neighbor nodes and their nearby nodes to estimate the status of the neighbor nodes;then we use the weighted fault-tolerant algorithm to predict the status of neighbor nodes for fault detection of the central node;by using Bayes method,the weight threshold for fault detecton is calculated.We build two kinds of fault-tolerant detection model:one is to simulate the wireless senser network with all sensers arranged regularly,and the other is to simulate the network with sensers arranged irregularly.Two weighted classification algorithm are proposed respectively to detect the event region of these two models:the algorithm with fix weight is for regular-arranged sensor network,the other algorithm based on distance-weight method is for irregular-aranged sensor network.The experiment results show that these two weighted classification algorithms gain high accuracy for fault-tolerant detection of the WSN event region,and cost low energy of the whole network.(2)Samples of multi-label data may belong to more than one class, so its classification problem is much more complicated than single-label data.This paper proposed a novel multi-label learning algorithm.Feature attributes of multi-label data often has high dimensions,and we use LLE algorithm to decrease the dimension in order to extract a group of low dimensional feature attributes set which could completely describe data. Then multi-label samples are partitioned in terms of their belonging classes,and learn classification characteristics of each group using Bayesian classification model.After that,we can get the final class-label set of multi-label samples according to the decision class-label of each classification model.In this paper,the algorithm is applied to multi-label classification learning of nature scene image and gene data individually. Experimental results show that our algorithm can acquire good classification effects on different multi-label dataset,and enable better performance compared to other similar algorithms.