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.
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.
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.
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.
42610771
A Novel Ordering-Based Greedy Bayesian Network Learning Algorithm on Limited Data
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.
21928964
Energy-aware optimal workload allocation among the battery-powered devices to maximize the co-operation life time
Traditional low power design minimizes the total power consumption. However for systems that want to maximize co-operation life time among the battery-powered devices, the total power minimization strategy may not be the best. The workload allocation among these devices has a dramatic impact on the co-operation life time. In this paper, we address this problem by formulating it as a linear programming (LP) problem. A simplified energy-aware algorithm is proposed to provide the optimal solution according to the energy capacity ratio of the devices. This algorithm is simple enough to be embedded into real-time system and we illustrate this by an example using a configurable transmitter-receiver pair. Experimental results show that the proposed algorithm significantly outperforms the other workload allocation strategies by 400.66% in the randomly-generated systems and 23.20% in a point-to-point (P2P) communication system, respectively
16252151
A Topology Optimization for Truss Based on Improved Group Search Optimizer
In this paper, a novel optimization algorithm, named group search optimizer (GSO) with two improvements, is used to truss structure topology optimization. And the improved methods are using harmony memory and adhering to the boundary which can improve the algorithm obviously. Then a simple and effective topology method, discretization of topology variables is incorporated with improved GSO to make sure that the topology optimization works well. In the end of the paper, two topology optimal models, a planar truss and a spatial truss, are used to test this topology methods based on the improved GSO. The results show that the method with improved GSO is feasible and robust for truss topology optimization.
127814276
Smarandache notions
18563923
Forward and Inverse Solutions of Electrocardiography Problem Using an Adaptive BEM Method
The construction of geometry models of heart-torso is critical for solving the forward and inverse problems of magneto- and electrocardiography (MCG/ECG). Boundary element method (BEM) is a commonly used numerical approach for the solution of these problems and it requires the modeling of interfaces between various tissue regions. In this study, a new BEM (h-adaptive type) has been applied to the ECG forward/inverse problems. Compared with those traditional BEMs, the adaptive BEM can self-adjust the number and size of the boundary element (BE) meshes according to an error indicator, and thus can save a lot of computational time and also improve the accuracy of the forward and inverse solutions. In this paper, the procedure of the adaptive triangular mesh generation is detailed and the algorithm is tested using a concentric sphere model and a realistic heart-torso model. For the realistic torso model, to improve the numerical accuracy, a number of new nodes are added on the basis of initial torso BE meshes, and the corresponding node coordinates are determined using an approach called Parametric Fourier Representation (PFR) of closed polygons. The simulation results show that the adaptive BEM is more accurate and efficient than traditional BEMs and therefore it is a very promising numerical scheme for ECG forward/inverse problems.
16024151
An adaptable architecture for secure delivery of converged services
We present an approach for secure delivery of multimodal services to end users over heterogeneous converged networks. Our approach leads to an easily adaptable and extensible system architecture, in which system components interact with each other through loosely coupled interfaces based on asynchronous exchange of messages. It also effectively separates service logic from service delivery, through the introduction of a secure broker between the end user devices and the enterprise level application that contains the service logic. This separation also provides additional levels of security at the application level. This paper describes how our approach provides the features mentioned above. We have also validated our approach through the implementation of a prototype system, a description of which is also provided.