標(biāo)題: Titlebook: Evolutionary Computation in Data Mining; Ashish Ghosh,Lakhmi C. Jain Book 2005 Springer-Verlag Berlin Heidelberg 2005 Data mining.Evolutio [打印本頁] 作者: crusade 時間: 2025-3-21 19:32
書目名稱Evolutionary Computation in Data Mining影響因子(影響力)
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書目名稱Evolutionary Computation in Data Mining網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Evolutionary Computation in Data Mining被引頻次
書目名稱Evolutionary Computation in Data Mining被引頻次學(xué)科排名
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書目名稱Evolutionary Computation in Data Mining讀者反饋
書目名稱Evolutionary Computation in Data Mining讀者反饋學(xué)科排名
作者: 天氣 時間: 2025-3-21 20:53 作者: Living-Will 時間: 2025-3-22 01:53 作者: Conspiracy 時間: 2025-3-22 07:01 作者: 事與愿違 時間: 2025-3-22 11:52
Genetic Programming in Data Mining for Drug Discovery,arning show no statistical difference between rats (albeit without known clearance differences) and man. Thus evolutionary computing offers the prospect of . ADME screening, e.g. for “virtual” chemicals, for pharmaceutical drug discovery.作者: vocation 時間: 2025-3-22 14:33 作者: vocation 時間: 2025-3-22 20:37 作者: Ventricle 時間: 2025-3-23 00:02
The Making of the EU’s Strategy Towards Asiathese modules, the rule-based evaluation criteria are designed in our mechanism. From our experiments, applying evolutionary algorithm to select critical financial ratios obtains better forecasting accuracy, and, a much better accuracy is obtained if more function modules are integrated in our mechanism.作者: Deference 時間: 2025-3-23 02:56
Evolutionary Computation in Intelligent Network Management, used to optimize the concurrent architecture of a fuzzy clustering algorithm (to discover data clusters) and a fuzzy inference system to analyze the trends. Empirical results clearly shows that evolutionary algorithm could play a major rule for the problems considered and hence an important data mining tool.作者: 膠水 時間: 2025-3-23 07:50
Microarray Data Mining with Evolutionary Computation,d performance is possible. In light of the overabundance of expression data, the application of methods of simulated evolution towards the development of better predictive models holds a promising future.作者: MEN 時間: 2025-3-23 12:35
An Evolutionary Modularized Data Mining Mechanism for Financial Distress Forecasts,these modules, the rule-based evaluation criteria are designed in our mechanism. From our experiments, applying evolutionary algorithm to select critical financial ratios obtains better forecasting accuracy, and, a much better accuracy is obtained if more function modules are integrated in our mechanism.作者: Comedienne 時間: 2025-3-23 14:13 作者: 先驅(qū) 時間: 2025-3-23 19:37
German Energy Policy in Transition,consideration to hidden relationships between features. A Genetic Algorithm is used to determine which set of features is the most predictive. Using ten well-known data sets we show that our approach, in comparison to C4.5 alone, provides marked improvement in a number of cases.作者: PTCA635 時間: 2025-3-23 22:44
Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining,ection algorithms applied in different size data sets to evaluate the scaling up problem. The results show that the stratified evolutionary instance selection algorithms consistently outperform the non-evolutionary ones. The main advantages are: better instance reduction rates, higher classification accuracy and reduction in resources consumption.作者: BALE 時間: 2025-3-24 05:38 作者: terazosin 時間: 2025-3-24 07:13
1434-9922 lutionary computation can be used for solving real-life probData mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, whi作者: DAMN 時間: 2025-3-24 12:42 作者: Heterodoxy 時間: 2025-3-24 18:26 作者: 付出 時間: 2025-3-24 20:14
Louka T. Katseli,Nicholas P. Glytsoss members plays a key role in minimizing the combined bias and variance of the ensemble. In this chapter, we compare between different mechanisms and methods for promoting diversity in an ensemble. In general, we found that it is important to design the diversity promoting mechanism very carefully for the ensemble’s performance to be satisfactory.作者: 同謀 時間: 2025-3-25 01:46 作者: 駕駛 時間: 2025-3-25 06:06
Ashish Ghosh,Lakhmi C. JainState of the art in the area of Data Mining and Knowledge Discovery with Evolutionary Algorithms.Demonstrates how the different tools of evolutionary computation can be used for solving real-life prob作者: 濕潤 時間: 2025-3-25 11:13 作者: 油氈 時間: 2025-3-25 15:18
978-3-642-42195-2Springer-Verlag Berlin Heidelberg 2005作者: 爭議的蘋果 時間: 2025-3-25 19:39
Evolutionary Computation in Data Mining978-3-540-32358-7Series ISSN 1434-9922 Series E-ISSN 1860-0808 作者: 設(shè)想 時間: 2025-3-25 20:31
https://doi.org/10.1007/3-540-32358-9Data mining; Evolutionary Computation; Knowledge Discovery in Databases; Multi-Agent Data mining; algori作者: gusher 時間: 2025-3-26 02:03 作者: Chemotherapy 時間: 2025-3-26 04:31 作者: semble 時間: 2025-3-26 10:30 作者: 表示問 時間: 2025-3-26 13:47
https://doi.org/10.1007/978-3-531-19201-7gorithms that build feature-vector-based classifiers in the form of rule sets. With the tremendous explosion in the amount of data being amassed by organizations of today, it is critically important that data mining techniques are able to process such data efficiently. We present the Distributed Lea作者: 光滑 時間: 2025-3-26 20:18
https://doi.org/10.1057/9780230306851ntally affect the accuracy of rules. Data dimensionality reduction is desirable as a preprocessing procedure for rule extraction. We propose a rule extraction system for extracting rules based on class-dependent features which is selected by genetic algorithms (GAs). The major parts of the rule extr作者: 箴言 時間: 2025-3-27 00:12
European Energy and Climate Securitysification technique, namely CORE (COevolutionary Rule Extractor), is proposed to discover cohesive classification rules in data mining. Unlike existing approaches where candidate rules and rule sets are evolved at different stages in the classification process, the proposed CORE coevolves rules and作者: 多嘴多舌 時間: 2025-3-27 04:42 作者: Ischemia 時間: 2025-3-27 07:47
https://doi.org/10.1007/978-3-319-97157-5ionary techniques have been used with success as global searchers in difficult problems, particularly in the optimization of non-differentiable functions. Hence, they can improve clustering. However, existing . clustering techniques suffer from one or more of the following shortcomings: (i) they are作者: N防腐劑 時間: 2025-3-27 09:40 作者: Benign 時間: 2025-3-27 16:49
The European Union and Harmonisation,h generalize to rats and to marketed drugs in humans. Receiver Operating Characteristics (ROC) curves for the binary classifier produced by machine learning show no statistical difference between rats (albeit without known clearance differences) and man. Thus evolutionary computing offers the prospe作者: 榨取 時間: 2025-3-27 20:38 作者: Harpoon 時間: 2025-3-28 01:07 作者: 提煉 時間: 2025-3-28 03:27 作者: voluble 時間: 2025-3-28 10:19
Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining,wed as a search problem, it could be solved using evolutionary algorithms..In this chapter, we have carried out an empirical study of the performance of CHC as representative evolutionary algorithm model. This study includes a comparison between this algorithm and other non-evolutionary instance sel作者: 無關(guān)緊要 時間: 2025-3-28 12:45
GAP: Constructing and Selecting Features with Evolutionary Computing,he use of Genetic Programming and a Genetic Algorithm to pre-process data before it is classified using the C4.5 decision tree learning algorithm. Genetic Programming is used to construct new features from those available in the data, a potentially significant process for data mining since it gives 作者: 牢騷 時間: 2025-3-28 15:52
Multi-Agent Data Mining using Evolutionary Computing,gorithms that build feature-vector-based classifiers in the form of rule sets. With the tremendous explosion in the amount of data being amassed by organizations of today, it is critically important that data mining techniques are able to process such data efficiently. We present the Distributed Lea作者: 引起 時間: 2025-3-28 22:36 作者: obviate 時間: 2025-3-28 23:22 作者: Vital-Signs 時間: 2025-3-29 05:42
Diversity and Neuro-Ensemble,ns. It has been shown that combining different neural networks can improve the generalization ability of learning machines. Diversity of the ensemble’s members plays a key role in minimizing the combined bias and variance of the ensemble. In this chapter, we compare between different mechanisms and 作者: GNAT 時間: 2025-3-29 10:28
Unsupervised Niche Clustering: Discovering an Unknown Number of Clusters in Noisy Data Sets,ionary techniques have been used with success as global searchers in difficult problems, particularly in the optimization of non-differentiable functions. Hence, they can improve clustering. However, existing . clustering techniques suffer from one or more of the following shortcomings: (i) they are作者: 土坯 時間: 2025-3-29 12:52 作者: 跑過 時間: 2025-3-29 19:36 作者: dictator 時間: 2025-3-29 23:12
Microarray Data Mining with Evolutionary Computation,umber of gene expressions coupled with analysis over a time course, provides an immense space of possible relations. Some small portion of this space contains information that is of extreme value to modern biomedicine in terms of proper diagnosis and treatment of many diseases. Classical methods of 作者: THE 時間: 2025-3-30 01:18 作者: LUDE 時間: 2025-3-30 06:11
https://doi.org/10.1057/9780230306851ion systems from the view point of its components. Then we propose a decompositional rule extraction method based on RBF neural networks. In the proposed rule extraction method, rules are extracted from trained RBF neural networks with class-dependent features. GA is used to determine the feature su
