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.
15250571
Frequent Itemset Mining Based on Heuristic Two Level Counting
Recently, many enchanced Apriori algorithms have been proposed to efficiently generate all frequent itemsets from datasets in data mining field. Although efficient techniques were presented, those algorithms are either time-consuming or memory-consuming. To address the issue further, a new algorithm, which introduced a novel support counting method, heuristic two level counting, is proposed. HTLC method adopts an improved itemset generating technology in the generation process of low level itemsets, which promotes the production of low level frequent itemsets or candidate itemsets. It also applies a heuristic traversal technology which speeds up one pass over datasets and support counting technology which largely reduces the number of passes over datasets to the generation of high level frequent itemsets. Finally, the experimental results show that it outperforms existing Apriori-like algorithms in mostly 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.
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.
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.
1280778
Splice Site Prediction using Support Vector Machines with Context-Sensitive Kernel Functions
This paper focuses on the use of support vector machines on a typical context-dependent classification task, splice site prediction. For this type of problems, it has been shown that a context-based approach should be preferred over a transfor- mation approach because the former approach can easily incorporate statistical mea- sures or directly plug sensitivity information into distance functions. In this paper, we designed three types of context-sensitive kernel functions: polynomial-based, radial basis function-based and negative distance-based kernels. From the experimental re- sults it becomes clear that the radial basis function-based kernel with information gain weighting gets the best accuracies and can always outperform their simple non-sensitive counterparts both in accuracy and in model complexity. And with well designed fea- tures and carefully chosen context sizes, our system can predict splice sites with fairly high accuracy, which can achieve the FP95% rate, 3.94 for donor sites and 5.98 for acceptor sites, an approximate state of the art performance for the moment.
14796999
Robust color-based tracking
Color as a distinct feature is widely used for object representation and tracking. However, color-based tracking is often influenced by clutter background and illumination variation. This paper presents a robust color-based tracking method, in which robust color feature is extracted for constructing the observation model under the modified particle filter tracking framework. The object is represented by its dominant color, and the weighted histogram with spatial information of the dominant color is used to optimize object models. In the particle filter framework, an extended iterated likelihood weighting scheme is employed to utilize more valuable particles. The experimental results show it is a real-time robust tracker, and it can obtain more than 30 fps with 2.4 G CPU and 512 MRAM.
14281509
A Quick Group Search Optimizer with Passive Congregation and its Convergence Analysis
This paper presents a quick group search optimizer with passive congregation (QGSOPC) for optimum design of pin connected structure. The algorithm is based on the quick group search optimizer (QGSO). The QGSOPC algorithm is verified and compared with the HPSO, GSO and QGSO algorithm used for the designs of two planar and spatial truss structures. The results show that the QGSOPC algorithm not only has preferable convergence rate and accuracy, but also it has the best stability of convergence rate and accuracy. It is desired for QGSOPC to be used more effectively for practical structural optimal design problems.
12282317
A note on “a mapping algorithm for computer-assisted exploration in the design of embedded systems”
The scaling of autoclave and leaching-equipment surfaces during the high pressure leaching of nickeliferous oxide and silicate ores is controlled during leaching to favor the formation of scale containing substantial amounts of magnesium sulfate that is more easily removed by chemical dissolution using water or dilute sulfuric acid at temperatures ranging from about 50 DEG C. to 250 DEG C. than scale containing substantial amounts of alunite.
27634891
Nonlinear Adaptive Blind Source Separation Based on Kernel Function
As the linear method is difficult to recover the sources from the nonlinear mixture signals, in this paper a new nonlinear adaptive blind signal separation algorithm based kernel space is proposed for general invertible nonlinearities. The received mixture signals are mapped from low dimensional space to high dimensional kernel feature space. In the feature space, the received signals form a smaller submanifold, and an orthonormal basis of the submanifold is constructed in this space, as the same time, the mixture signals are parameterized by the basis in the high dimensional kernel space. In the noiseless or noisy situation, the sources are rebuilt online processing by M-EASI and subspace tracking. The results of computer simulations are also presented.