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991.
Purpose
Extracting comprehensible classification rules is the most emphasized concept in data mining researches. In order to obtain accurate and comprehensible classification rules from databases, a new approach is proposed by combining advantages of artificial neural networks (ANN) and swarm intelligence.Method
Artificial neural networks (ANNs) are a group of very powerful tools applied to prediction, classification and clustering in different domains. The main disadvantage of this general purpose tool is the difficulties in its interpretability and comprehensibility. In order to eliminate these disadvantages, a novel approach is developed to uncover and decode the information hidden in the black-box structure of ANNs. Therefore, in this paper a study on knowledge extraction from trained ANNs for classification problems is carried out. The proposed approach makes use of particle swarm optimization (PSO) algorithm to transform the behaviors of trained ANNs into accurate and comprehensible classification rules. Particle swarm optimization with time varying inertia weight and acceleration coefficients is designed to explore the best attribute-value combination via optimizing ANN output function.Results
The weights hidden in trained ANNs turned into comprehensible classification rule set with higher testing accuracy rates compared to traditional rule based classifiers. 相似文献992.
未登录词(out of vocabulary,OOV)的查询翻译是影响跨语言信息检索(cross-language information retrieval,CLIR)性能的关键因素之一.它根据维基百科(Wikipedia)的数据结构和语言特性,将译文环境划分为目标存在环境和目标缺失环境.针对目标缺失环境下的译文挖掘难点,它采用频度变化信息和邻接信息实现候选单元抽取,并建立基于频度-距离模型、表层匹配模板和摘要得分模型的混合译文挖掘策略.实验将基于搜索引擎的未登录词挖掘技术作为baseline,并采用TOP1进行评测.实验验证基于维基百科的混合译文挖掘方法可达到0.6822的译文正确率,相对baseline取得6.98%的改进. 相似文献
993.
针对传统隐马尔可夫模型(HMM)状态转移概率仅与前一状态有关的不足,提出了一种改进的隐马尔可夫模型(Im-proved-HMM),该模型考虑到状态转移概率与前两时刻状态相关,旨在提高异常检测准确率。用基于Improved-HMM的Baum-Welch(BW)算法对正常进程行为进行建模,并采用滑动窗口的方法,检测进程行为是否处于异常状态。实验结果表明,该模型的检测准确率高于传统的HMM模型,能及时、准确检测到进程行为的异常。 相似文献
994.
针对关联规则挖掘中,基于支持度-置信度框架的关联规则评价标准存在缺乏具体应用领域的分析,挖掘结果很难用于用户决策等问题,提出一种面向领域关联规则评价方法。该方法以领域知识为基准,发现满足技术兴趣度和商业兴趣度的规则,以国家住宅工程中心40个健康住宅试点项目的实际调查数据为例,进行试验和分析。在此基础上,设计并开发了居住健康领域挖掘系统,该系统采用多层次软件架构,包括知识库管理、挖掘数据选择、数据预处理、领域挖掘和结果评价等功能。实验结果和系统应用结果表明了面向领域关联规则评价方法的有效性。 相似文献
995.
为了提高背包加密体制的安全性,对基于超递增序列的背包加密算法进行了分析,指出了利用非超递增序列构造背包所存在的难题,提出一种无冲突非超递增序列的构造方法,并给出严格的证明。依据该方法提出了一种基于无冲突非超递增序列的背包公钥加密算法,有效地避免了利用非超递增序列构造背包的过程中出现的难题。理论分析和仿真实验结果表明,该算法具有高的安全性能,在抵抗Shamir攻击和低密度攻击方面都具有良好的性能。 相似文献
996.
997.
Workflow simulation for operational decision support 总被引:1,自引:0,他引:1
Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for operational decision making and continuous improvement. Here we describe a simulation system for operational decision support in the context of workflow management. To do this we exploit not only the workflow’s design, but also use logged data describing the system’s observed historic behavior, and incorporate information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM. 相似文献
998.
In this paper, we propose an efficient algorithm, called CMP-Miner, to mine closed patterns in a time-series database where each record in the database, also called a transaction, contains multiple time-series sequences. Our proposed algorithm consists of three phases. First, we transform each time-series sequence in a transaction into a symbolic sequence. Second, we scan the transformed database to find frequent patterns of length one. Third, for each frequent pattern found in the second phase, we recursively enumerate frequent patterns by a frequent pattern tree in a depth-first search manner. During the process of enumeration, we apply several efficient pruning strategies to remove frequent but non-closed patterns. Thus, the CMP-Miner algorithm can efficiently mine the closed patterns from a time-series database. The experimental results show that our proposed algorithm outperforms the modified Apriori and BIDE algorithms. 相似文献
999.
José Luis Crespo Marta Zorrilla Pilar Bernardos Eduardo Mora 《The Visual computer》2009,25(4):309-323
The objective of this paper is to present an overall approach to forecasting the future position of the moving objects of
an image sequence after processing the images previous to it. The proposed method makes use of classical techniques such as
optical flow to extract objects’ trajectories and velocities, and autoregressive algorithms to build the predictive model.
Our method can be used in a variety of applications, where videos with stationary cameras are used, moving objects are not
deformed and change their position with time. One of these applications is traffic control, which is used in this paper as
a case study with different meteorological conditions to compare with.
相似文献
Marta Zorrilla (Corresponding author)Email: |
1000.
Themis P. Exarchos Markos G. Tsipouras Costas Papaloukas Dimitrios I. Fotiadis 《Knowledge and Information Systems》2009,19(2):249-264
In this paper we present a novel methodology for sequence classification, based on sequential pattern mining and optimization
algorithms. The proposed methodology automatically generates a sequence classification model, based on a two stage process.
In the first stage, a sequential pattern mining algorithm is applied to a set of sequences and the sequential patterns are
extracted. Then, the score of every pattern with respect to each sequence is calculated using a scoring function and the score
of each class under consideration is estimated by summing the specific pattern scores. Each score is updated, multiplied by
a weight and the output of the first stage is the classification confusion matrix of the sequences. In the second stage an
optimization technique, aims to finding a set of weights which minimize an objective function, defined using the classification
confusion matrix. The set of the extracted sequential patterns and the optimal weights of the classes comprise the sequence
classification model. Extensive evaluation of the methodology was carried out in the protein classification domain, by varying
the number of training and test sequences, the number of patterns and the number of classes. The methodology is compared with
other similar sequence classification approaches. The proposed methodology exhibits several advantages, such as automated
weight assignment to classes using optimization techniques and knowledge discovery in the domain of application.
相似文献
Dimitrios I. FotiadisEmail: |