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Attribute reduction is one of the main issues in the theoretical research of rough set theory which is known as a NP-hard optimization problem. The objective is to find the minimal number of attributes from a large dataset. Hence it is difficult to solve to optimality. This paper proposes a composite neighbourhood structure approach to solve the attribute reduction problem that consists of two versions...
Bayesian network is an uncertainty inference network based on probability. Its structure learning is one of the main research techniques in the field of data mining and knowledge discovering, while constructing Bayesian network structures from data is NP hard. According to the information theory and conditional independence test, a new algorithm is presented for the construction of optimal Bayesian...
According to existing have defects discernibility matrix, and the attribute reduction algorithm for attribute reduction algorithm of complex process. This paper made part of optimization, based on the condition attributes classify the grouping generated representative data to simplify the discernibility matrix, and the order of the discernibility matrix, and the complexity of the attribute reduction...
Mining associate rules is an important research topic in Data Mining. It is a NP-hard problem to estimate whether there are frequent items which has t-attribute and σ - confidence in database. The important research fields of mining frequent items is to reduce the number of scanning database to improve the algorithm efficiency, the attribute Union theory is proposed to calculate the frequent items...
Hardware/software (HW/SW) partitioning is one of the key challenges in HW/SW codesign. This paper presents efficient algorithms for the HW/SW partitioning problem, which has been proved to be NP-hard. We reduce the HW/SW partitioning problem to a variation of knapsack problem that is approximately solved by searching 1D solution space, instead of searching 2D solution space in the latest work cited...
This paper considers the NP-hard problem of object assignment with respect to multiple constraints: assigning a set of elements (or objects) into mutually exclusive classes (or groups), where the elements which are ldquosimilarrdquo to each other are hopefully located in the same class. The literature reports solutions in which the similarity constraint consists of a single index that is inappropriate...
RNAi is a naturally occurring, highly conserved phenomenon of RNA mediated gene silencing among the multicellular organisms. Currently, RNAi has been successfully applied in functional genomics, therapeutics and new drug target identification in mammals and other eukaryotes. The uniqueness lies in sequence specific gene knock down which made RNAi an indispensible technology. In the mechanism of RNAi,...
Set Covering Problem and Set Partitioning Problem are models for many important industrial applications. In this paper, we solve some Operational Research benchmarks with Ant Colony Optimization using a new transition rule. A Lookahead mechanism was incorporated to check constraint consistency in each iteration. Computational results are presented showing the advantages to use this additional mechanism...
We propose a new distributed algorithm for sparse variants of the network alignment problem, which occurs in a variety of data mining areas including systems biology, database matching, and computer vision. Our algorithm uses a belief propagation heuristic and provides near optimal solutions for this NP-hard combinatorial optimization problem. We show that our algorithm is faster and outperforms or...
In this paper, we analyze the verification of K-step opacity in discrete event systems that are modeled as (possibly non-deterministic) finite automata with partial observation on their transitions. A system is K-step opaque if the entrance of the system state within the last K observations to a set of secret states remains opaque to an intruder who has complete knowledge of the system model and observes...
The main objective of sensor deployment problem in Wireless Sensor Network (WSN) is to use minimum number of sensor nodes with given sensing range that can cover any target in the coverage area to monitor the environment. The optimal sensor deployment enables accurate sensing information on target behavior with minimum sensing range and number of sensor nodes. The target coverage terrain in a locality...
Orthogonal frequency division multiplexing (OFDM) has attracted much attention in wireless communications due to its many advantages. However, OFDM is very sensitive to carrier frequency offset (CFO) which can destroy the orthogonality among subchannels. For OFDMA uplink systems with generalized carrier assignment scheme (GCAS), there are multiple CFOs need to be estimated simultaneously. While ML...
In this paper, a solution is proposed for n-Queen problem based on ACO (ant colony optimization). The n-Queen problem become intractable for large values of `n' and thus placed in NP (non-deterministic polynomial) class problem. The n-Queen problem is basically a generalized form of 8-Queen problem. In 8-Queen problem, the goal is to place 8 queens such that no queen can kill the other using standard...
We describe a generative model for graph edges under specific degree distributions which admits an exact and efficient inference method for recovering the most likely structure. This binary graph structure is obtained by reformulating the inference problem as a generalization of the polynomial time combinatorial optimization known as b-matching. Standard b-matching recovers a constant-degree constrained...
In a multi-parameter learning problem, besides choosing the architecture of the learner, there is the problem of finding the optimal parameters to get maximum performance. When the number of parameters to be tuned increases, it becomes infeasible to try all the parameter sets, hence we need an automatic mechanism to find the optimum parameter setting using computationally feasible algorithms. In this...
With the popularity of urban private car, urban road resources become limited. How to enhance the efficiency of road transportation with the existing road resources is one of today's academic research hot spots. To solve this problem, this paper proposes a new algorithm for dynamic route guidance, in order to provide 'the best path' for drivers The algorithm is based on ant colony optimization, using...
In this paper, a filter and fan method is proposed for the single-machine tardiness scheduling problem with sequence-dependent setups, which is a typical NP-hard combinational optimization problem. The method searches the solution space by means of neighborhood search tree and the tree branches are extended by predefined moves. To enhance the algorithm ability of escaping from local optima, kick strategy...
It is well known that reasoning with AI temporal projection problems is difficult. Determining the Possible Truth problem, a basic temporal projection decision problem, in the so-called Simple Event System remains NP-complete. In this paper, two types of constraints, on the graph-theoretic representation of the cause-and-effect relationships between events and on the partial orders of events, are...
Feature extraction is the transformation of high-dimensional data into a meaningful representation of reduced dimensionality. The representation extracted are often beneficial to mitigate the computational complexity and improve the accuracy of a particular classifier. In this paper we introduce a novel feature extraction algorithm called K nearest neighbor local margin maximization and apply it to...
A Covering Array denoted by CA(N; t,k,??) is a matrix of size N ?? k, where each tuple of t columns has at least one time each of the vt combinations of symbols. The C As are combinatorial objects used for software testing and design of experiments in: biology, agriculture, medicine, etc. CAs can be constructed using heuristic algorithms, greedy search and algebraic procedures. The Hartman Style Rising...
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