35120658
Max-Relevance and Min-Redundancy Greedy Bayesian Network Learning on High Dimensional Data
Existing algorithms for learning Bayesian network require a lot of computation on high dimensional itemsets which affects accuracy especially on limited datasets and takes up a large amount of time. To address the above problem, we propose a novel Bayesian network learning algorithm MRMRG, Max Relevance-Min Redundancy Greedy. MRMRG algorithm is a variant of K2 which is a well- known BN learning algorithm. We also analyze the time complexity of MRMRG. The experimental results show that MRMRG algorithm has much better efficiency and accuracy than most of existing algorithms on limited datasets.
7806615
A Novel Ordering-Based Greedy Bayesian Network Learning Algorithm on Limited Data
Existing algorithms for learning Bayesian network (BN) require a lot of computation on high dimensional itemsets, which affects accuracy especially on limited datasets and takes up a large amount of time. To alleviate the above problem, we propose a novel BN learning algorithm OM- RMRG, Ordering-based Max Relevance and Min Redun- dancy Greedy algorithm. OMRMRG presents an ordering- based greedy search method with a greedy pruning proce- dure, applies Max-Relevance and Min-Redundancy feature selection method, and proposes Local Bayesian Increment function according to Bayesian Information Criterion (BIC) formula and the likelihood property of overfitting. Exper- imental results show that OMRMRG algorithm has much better efficiency and accuracy than most of existing BN learning algorithms on limited datasets.
1975396
An Improved Greedy Bayesian Network Learning Algorithm on Limited Data
Although encouraging results have been reported, existing Bayesian network (BN) learning algorithms have some troubles on limited data. A statistical or information theoretical measure or a score function may be unreliable on limited datasets, which affects learning accuracy. To alleviate the above problem, we propose a novel BN learning algorithm MRMRG, Max Relevance and Min Redundancy Greedy algorithm. MRMRG algorithm applies Max Relevance and Min Redundancy feature selection technique and proposes Local Bayesian Increment (LBI) function according to the Bayesian Information Criterion (BIC) formula and the likelihood property of overfitting. Experimental results show that MRMRG algorithm has much better accuracy than most of existing BN learning algorithms when learning BNs from limited datasets.
7718117
Ensembling Bayesian network structure learning on limited data
In recent years, Bagging method has been applied to learn Bayesian networks (BNs), especially on limited datasets. However, the BNs learned using Bagging method from limited datasets can be biased towards complex models. We present an efficient approach to produce more accurate BNs from limited datasets. Based on the Markov condition of BN learning, we proposed a novel sampling method, called Root Nodes based Sampling (RNS), and a BNs fusion method. The experimental results reveal that our ensemble method can achieve more accurate results in terms of accuracy on limited datasets.
45720537
Bayesian Network Structure Ensemble Learning
Bayesian networks (BNs) have been widely used for learning model structures of a domain in the area of data mining and knowledge discovery. This paper incorporates ensemble learning into BN structure learning algorithms and presents a novel ensemble BN structure learning approach. Based on the Markov condition and the faithfulness condition of BN structure learning, our ensemble approach proposes a novel sample decomposition technique and a components integration technique. The experimental results reveal that our ensemble BN structure learning approach can achieve an improved result compared with individual BN structure learning approach in terms of accuracy.
11096456
Learning Bayesian Networks with Combination of MRMR Criterion and EMI Method
Currently, learning Bayesian Networks (BNs) from data has become a much attention-getting issue in fields of machine learning and data mining. While there exists few efficient algorithms for learning BNs in presence of incomplete data. In this paper, we present a scoring function based on mutual information for evaluating BN structures. To decrease computational complexity, we introduce MRMR criterion into the scoring function, which enables the computation of the scoring function to involve in only two-dimensional mutual information. When the dataset is incomplete, we use EMI method to estimate the Mutual Information (MI) from the incomplete dataset. As for whether a node ordering is manually given or not, we develop two versions of algorithms, named as MRMR- E1 and MRMR-E2 respectively and evaluate them through experiments. The experimental results on Alarm network show good accuracy and efficiency of our algorithms.
9822006
Named Entity Recognition in Biomedical Literature Using Two-Layer Support Vector Machines
A beam receiver secured to an upper portion of a blade of an earth working equipment can rotate in a horizontal plane and move in a vertical direction and, accordingly, can catch a laser beam from a beam projector regardless of a direction of the equipment. When the beam has been deviated out of the beam receiver, the direction of the deviation is detected so that the beam can be received by the beam receiver again. The height of the beam receiver relative to the blade can be adjusted and the level of the blade can thereby be controlled very accurately.
29468800
SceBoost Learning Algorithm for Feature Selection
This paper proposes an improved boost learning algorithm, the SceBoost algorithm, and its application in developing fast and robust features for citrus canker detection by machine vision. The algorithm use symmetric cross entropy to eliminate redundancy among selected features using AdaBoost algorithm. Selected features are subjected to recognize citrus canker symptoms on given pictures of citrus foliage. Compared with related feature selection algorithm our method can get improvements in classification accuracy and significantly reduce computation time when reach the same requirements.
14744630
TARGET: Two-way Web Service Router Gateway
In this paper, we present the approach and architecture of TARGET: two-way Web service router gateway, for two-way Web service interaction crossing enterprise domain and firewall. It provides a full support for asynchronous outbound operation and event notification in communication services. TARGET addresses an acute issue for Internet applications that today's enterprise NATs and firewalls only allow outbound HTTP request from the inside to the outside and block any request from the outside to the inside, which is a serious problem for two-way Web services. TARGET is a generic solution to allow two-way Web service interaction to traverse legitimately through NATs and strictly configured firewalls; and it is based on two-way SOAP message tunneling, service local registry, and service routing to bridge two-way Web service interaction. A research TARGET system has been implemented and applied to real time communication services, e.g. conferencing. Extensive experiments on TARGET are performed, and its performance with various sizes of SOAP messages is studied. The applicability and feasibility of TARGET for two-way Web service interaction is verified
36831968
Evaluating and Comparing Biomedical Term Identification Systems
In this paper, we propose a term identification system using conditional random fields (CRFs) on two biomedical datasets. Through employing several sets of experiments, we make a comprehensive investigation for different types of features. The final experimental results reflect that with carefully designed features i.e., adding not only the individual and dynamic features but also the combinational features, our system can identify biomedical terms with fairly high accuracy on both datasets, compared with other top systems already published in the literature.
5390173
A Self-supervised Learning Framework for Classifying Microarray Gene Expression Data
It is important to develop computational methods that can effectively resolve two intrinsic problems in microarray data: high dimensionality and small sample size. In this paper, we propose a self-supervised learning framework for classifying microarray gene expression data using Kernel Discriminant-EM (KDEM) algorithm. This framework applies self-supervised learning techniques in an optimal nonlinear discriminating subspace. It efficiently utilizes a large set of unlabeled data to compensate for the insufficiency of a small set of labeled data and it extends linear algorithm in DEM to kernel algorithm to handle nonlinearly separable data in a lower dimensional space. Extensive experiments on the Plasmodium falciparum expression profiles show the promising performance of the approach.