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.
10413997
The Max-Relevance and Min-Redundancy Greedy Bayesian Network Learning Algorithm
Existing algorithms for learning Bayesian network require a lot of computation on high dimensional itemsets which affects reliability, robustness and accuracy of these algorithms and takes up a large amount of time. To address the above problem, we propose a new 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. It is also shown that MRMRG algorithm has better accuracy than most of existing learning algorithms for limited sample datasets.
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.
42610771
A Novel Ordering-Based Greedy Bayesian Network Learning Algorithm on Limited Data
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.
27471784
A Novel Greedy Bayesian Network Structure Learning Algorithm for 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 MRMRG, Max Relevance and Min Redundancy Greedy algorithm. MRMRG algorithm is a variant of K2 algorithm for learning BNs from limited datasets. 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 efficiency and accuracy than most of existing BN learning algorithms when learning BNs from limited datasets.
11689877
A Projection and Search Algorithm for the Bicluster Problem
The advent of microarray technologies has made the experimental study of gene expression faster and more efficient. It is an important task to analyze the gene expression data acquired from the microarray experiments. Biclustering is a popular method to search a subset of genes which exhibit similar expression patterns along a subset of conditions in the gene expression matrix. In this paper, we propose a projection and search algorithm (PSA) for finding biclusters in the gene expression matrix. The algorithm adopts projection method to find a raw submatrix, then refines and extends the raw submatrix into a bicluster using greedy algorithms. We test the algorithm on simulation data and real data. The test results show that the algorithm has good performance on both simulation data and real data. Additionally, compared with other methods, PSA isnpsilat sensitive to noise level.
127814276
Smarandache notions
8081608
An analytic model for Ge/Si core/shell nanowire MOSFETs considering drift-diffusion and ballistic transport
In this paper an analytic model for Ge/Si core/shell nanowire MOSFETs (NWFETs) is developed. First, the electrostatic potential and charge model are derived out from classical device physics. Then the drift-diffusion drain current model is obtained and verified by comparisons with the numerical simulation. The ballistic current model is obtained with the approximately described quantummechanical effect through modifying the intrinsic carrier concentration under 2-dimensional confinement. With the proposed model, the performances of Ge/Si core/shell NWFETs are analyzed and significant characteristics are demonstrated in details. The analytic model in this paper provides a framework for further developing compact models of the NWFETs with Ge/Si core/shell heterostructure for circuit design and simulation.
17180210
An efficient distributed algorithm for constructing delay- and degree-bounded application-level multicast tree
In this paper we investigate the problem of finding a delay- and degree-bounded maximum sum of nodes application level multicast tree. We then proved the problem is NP-hard, and its relationship with the well-studied degree-bounded minimum maximum-delay multicast tree problem. We propose a distributed algorithm - OverStream, which adopts PPAF heuristic for father node to choose optimal child nodes based on multiple network parameters. Simulation results show that our algorithm is better than OMNI, SpreadIt, HMTP, and Zigzag in general.
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.