检索结果-内蒙古大学图书馆

A survey on algorithm adaptation in evolutionary computation

Frontiers of Electrical and Electronic Engineering in China 2012年第1期7卷 16-31页

作者： Jun ZHANG Wei-Neng CHEN Zhi-Hui ZHAN Wei-Jie YU Yuan-Long LI Ni CHEN Qi ZHOU Department of Computer Science Sun Yat-sen UniversityGuangzhou 51027.5 China Key Laboratory of Digital Life Ministry of Education Cluangzhou510275 China Key Laboratory of Software Technology Education Departmentof Guangdong Province Guangzhou 510275 China

Evolutionary computation （EC） is one of the fastest growing areas in computer science that solves intractable optimization problems by emulating biologic evolution and organizational behaviors in nature. To de- sign an EC algorithm, one needs to determine a set of algorithmic configurations like operator selections and parameter settings. How to design an effective and ef- ficient adaptation scheme for adjusting the configura- tions of EC algorithms has become a significant and promising research topic in the EC research community. This paper intends to provide a comprehensive survey on this rapidly growing field. We present a classification of adaptive EC （AEC） algorithms from the perspective of how an adaptation scheme is designed, involving the adaptation objects, adaptation evidences, and adapta- tion methods. In particular, by analyzing tile popula- tion distribution characteristics of EC algorithms, we discuss why and how the evolutionary state information of EC can be estimated and utilized for designing ef- fective EC adaptation schemes. Two AEC algorithms using the idea of evolutionary state estimation, includ- ing the clustering-based adaptive genetic algorithm and the adaptive particle swarm optimization algorithm are presented in detail. Some potential directions for the re- search of AECs are also discussed in this paper.

关键词： evolutionary algorithm (EA) evolution- ary computation (EC) algorithm adaptation parameter control

来源：评论

学校读者我要写书评

暂无评论

High-Performance algorithm adaptations and Hardware Architecture for HEVC Intra Encoders

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2019年第7期29卷 2138-2145页

作者： Zhang, Yuanzhi Lu, Chao Southern Illinois Univ Carbondale Dept Elect & Comp Engn Carbondale IL 62901 USA

High computational complexity and heavy data/timing dependency in high efficiency video coding (HEVC) hinder the throughput of video encoding. In order to address both design challenges, we present high-performance algorithm adaptations and high-throughput hardware architecture for the HEVC intra encoders. Particularly, the chroma intra prediction includes the derived luma mode, which greatly improves the worst-case video compression performance. Compared with the HM-15.0 reference software, the proposed algorithms reduce the computational workload by 22%, while the average BD-Rate and BD-PSNR for classes A-F and 4K are 2.79% and -0.13 dB, respectively. This BD-Rate is lower than existing designs in the literature with the same video resolution. The field-programmable gate array (FPGA) implementation of proposed design shows it operates at 120 MHz and supports 45 f/s of 1080P video sequences using 196K logic elements and 127-KB on-chip SRAM. The application-specified integrated circuit (ASIC) implementation of the proposed design in TSMC 90 nm technology shows that its clock frequency reaches 320 MHz with a hardware gate count of 2186K and power consumption of 290 mW, and it supports real-time encoding of 4K videos at 30 f/s.

关键词： HEVC intra encoder algorithm adaptation derived luma mode hardware architecture worst case BD-Rate

来源：评论

学校读者我要写书评

暂无评论

adaptation of algorithms for Diagnostics of Steam Turbine Unit Equipment to Specific Conditions at Thermal Power Stations

引用

THERMAL ENGINEERING 2020年第11期67卷 800-804页

作者： Aronson, K. E. Murmansky, B. E. Novoselov, V. B. Brodov, Yu. M. Sosnovsky, A. Yu. Murmanskii, I. B. Izotin, D. A. Ural Fed Univ Ural Power Engn Inst Ekaterinburg 620002 Russia Sverdlovskii T Plus Grp Ekaterinburg 620219 Russia Teploenergoserv Management Co Ekaterinburg 620014 Russia Ural Turbine Works Ekaterinburg 620017 Russia

At present, steam turbine manufacturers are interested in delivering diagnostic equipment systems for steam turbine units (STU) together with automatic control systems. These diagnostic systems should be adapted to a specific turbine size (or modification). The diagnostic system adaptation depends on the algorithm type used to detect defects. The following types of algorithms can be singled out: computational ones determining the defect using a computational model; expert ones based on probabilistic methods (using the summation of weighting coefficients of malfunction symptoms and the Bayes theorem); and digital ones based on the phenomenological relationships among process parameters, equipment state parameters, and other indicators obtained over the test period of equipment's operation. The computational algorithms can be integrated into the turbine APCS and executed online (such as control system algorithms and steam admission system algorithms) or be used for handling postoperative tasks (such as diagnostics of auxiliary equipment or solving technical and economic problems). Expert algorithms are employed when development of a computational model involves great difficulties (e.g., in diagnosing a turbine thermal-expansion monitoring system or vibration-monitoring system). Digital algorithms are not related to the type of tasks, to specific equipment, or its features or operating modes. The degree of definiteness of a detected defect depends on the type of algorithm. The need to adapt diagnostic systems to other equipment involves not only the development of new diagnostic algorithms but also the formalization of the discourse. By this is meant the selection and justification of a standard state (prototype), i.e., a model state, to be used as a reference for development of diagnostic symptoms and a digital description of defect symptoms determined from the measurements and expert observations and opinions required for diagnosing.

关键词： steam turbine unit equipment diagnosing algorithm adaptation state assessment diagnostic symptoms defects

来源：评论

学校读者我要写书评

暂无评论

Empowering Random Walk Link Prediction algorithms in Complex Networks by Adapted Structural Information

引用

IEEE ACCESS 2024年 12卷 45044-45059页

作者： Dimitriou, Paraskevas Karyotis, Vasileios Ionian Univ Dept Informat Corfu 49100 Greece

In the link prediction problem a relevant algorithm running over a network attempts to determine whether a link between two nodes will exist in the future, given that it is not present at the moment. Most link prediction algorithms take into account the structure of the network on which they are applied and based on this, they attempt to predict the existence or not of future new edges in the network. However, many of them are quite standardized, applying the same concept and parametrization to all networks, thus not always achieving good results in every different network structure. algorithms based on Graph Neural Networks (GNNs) are more adaptive to any network structure but they do not give appreciable results when the only information available is the network structure. In this paper, we propose a new approach to this problem that approximates the structure of a complex network by allowing adjusted weight to this network structure to create additional information, which we can embed into effective algorithms such as local and superposed random walk link prediction. To achieve this goal, we use well-known kernel functions such as Sigmoids, in which we fit their parameters appropriately by a genetic algorithm to achieve the best possible approximation. To demonstrate the effectiveness of our proposed method we have compared our prediction method results based on precision, AUC and AUPR on eleven selected networks of different structures and properties with seven well-known link prediction algorithms and one more utilizing GNNs. In every case, we have improved the results of random walk algorithms and in most cases we achieved better results from all employed benchmark algorithms.

关键词： Prediction algorithms Complex networks Indexes Graph neural networks Approximation algorithms Genetic algorithms Measurement link prediction node similarity sigmoid function genetic algorithms algorithm adaptation

来源：评论

学校读者我要写书评

暂无评论

Multi-label learning for identifying co-occurring class code smells

引用

COMPUTING 2024年第8期106卷 2585-2612页

作者： Hadj-Kacem, Mouna Bouassida, Nadia Sfax Univ Mir Cl Lab Sfax Tunisia

Code smell identification is crucial in software maintenance. The existing literature mostly focuses on single code smell identification. However, in practice, a software artefact typically exhibits multiple code smells simultaneously where their diffuseness has been assessed, suggesting that 59% of smelly classes are affected by more than one smell. So to meet this complexity found in real-world projects, we propose a multi-label learning-based approach to identify eight code smells at the class-level, i.e. the most sever software artefacts that need to be prioritized in the refactoring process. In our experiments, we have used 12 algorithms from different multi-label learning methods across 30 open-source Java projects, where significant findings have been presented. We have explored co-occurrences between class code smells and examined the impact of correlations on prediction results. Additionally, we assess multi-label learning methods to compare data adaptation versus algorithm adaptation. Our findings highlight the effectiveness of the Ensemble of Classifier Chains and Binary Relevance in achieving high-performance results.

关键词： Multi-label learning Class code smells Problem transformation algorithm adaptation

来源：评论

学校读者我要写书评

暂无评论

A Review on Multi-Label Learning algorithms

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2014年第8期26卷 1819-1837页

作者： Zhang, Min-Ling Zhou, Zhi-Hua Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Jiangsu Peoples R China Southeast Univ Minist Educ Key Lab Comp Network & Informat Integrat Nanjing 210096 Jiangsu Peoples R China Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China

Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been made toward this emerging machine learning paradigm. This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms. Firstly, fundamentals on multi-label learning including formal definition and evaluation metrics are given. Secondly and primarily, eight representative multi-label learning algorithms are scrutinized under common notations with relevant analyses and discussions. Thirdly, several related learning settings are briefly summarized. As a conclusion, online resources and open research problems on multi-label learning are outlined for reference purposes.

关键词： Multi-label learning label correlations problem transformation algorithm adaptation

来源：评论

学校读者我要写书评

暂无评论

Applying multi-label techniques in emotion identification of short texts

引用

NEUROCOMPUTING 2018年 320卷 35-46页

作者： Almeida, Alex M. G. Cerri, Ricardo Paraiso, Emerson Cabrera Mantovani, Rafael Gomes Barbon Junior, Sylvio State Univ Londrina UEL Dept Comp Sci BR-86057970 Londrina Brazil Fed Univ Sao Carlos UFSCar Dept Comp Sci BR-13565905 Sao Carlos SP Brazil Pontificia Univ Catolica Parana PUCPR Grad Program Informat BR-80215901 Curitiba Parana Brazil Univ Sao Paulo Inst Math & Comp Sci ICMC BR-13566590 Sao Carlos SP Brazil

Sentiment Analysis is an emerging research field traditionally applied to classify opinions, sentiments and emotions towards polarity and subjectivity expressed in text. An important characteristic to automatic emotion analysis is the standpoint, in which we can look at an opinion from two perspectives, the opinion holder (author) who express an opinion, and the reader who reads and perceives the opinion. From the reader's standpoint, the interpretations of the text can be multiple and depend on the personal background. The multiple standpoints cognition, in which readers can look at the same sentence, is an interesting scenario to use the multi-label classification paradigm in the Sentiment Analysis domain. This methodology is able to handle different target sentiments simultaneously in the same text, by also taking advantage of the relations between them. We applied different approaches such as algorithm adaptation, problem transformation and ensemble methods in order to explore the wide range of multi-label solutions. The experiments were conducted on 10,080 news sentences from two different real datasets. Experimental results showed that the Ensemble Classifier Chain overcame the other algorithms, average F-measure of 64.89% using emotion strength features, when considering six emotions and neutral sentiment. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Sentiment analysis Back propagation multi-label learning Multi-label k-Nearest Neighbor Hierarchy of multi-label classifier algorithm adaptation Problem transformation

来源：评论

学校读者我要写书评

暂无评论

Dynamic mutual information-based feature selection for multi-label learning

引用

INTELLIGENT DATA ANALYSIS 2023年第4期27卷 891-909页

作者： Kim, Kyung-Jun Jun, Chi-Hyuck Pohang Univ Sci & Technol POSTECH Dept Ind & Management Engn Pohang South Korea

In classification problems, feature selection is used to identify important input features to reduce the dimensionality of the input space while improving or maintaining classification performance. Traditional feature selection algorithms are designed to handle single-label learning, but classification problems have recently emerged in multi-label domain. In this study, we propose a novel feature selection algorithm for classifying multi-label data. This proposed method is based on dynamic mutual information, which can handle redundancy among features controlling the input space. We compare the proposed method with some existing problem transformation and algorithm adaptation methods applied to real multi-label datasets using the metrics of multi-label accuracy and hamming loss. The results show that the proposed method demonstrates more stable and better performance for nearly all multi-label datasets.

关键词： Feature selection multi-label learning dynamic mutual information filter ranking algorithm adaptation

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Multi-label Classification algorithms 1

Comparative Analysis of Multi-label Classification Algorithm...

引用

1st International Conference on Secure Cyber Computing and Communications (ICSCCC)

作者： Sharma, Seema Mehrotra, Deepti Amity Univ Noida Uttar Pradesh India

ISBN: (纸本)9781538663738

Multi-label classification has generated enthusiasm in many fields over the last few years. It allows the classifications of dataset where each instance can be associated with one or more label. It has successfully ended up being superiorstrategy as compared to Single labelclassification. In this paper, we provide an overview of multi-label classification approaches. We also discussed the various tools thatutilizes MLC approaches. Lastly, we have presented an experimental study to compare different algorithms of multi-label classification. After applying and studying the accuracies of various multilabel classification techniques, we have found that performance of Random Forest is better than the rest of the other compared multilabelclassification algorithms with 96% accuracy.

关键词： Multi-label classification (MLC) Multi-label dataset (MLD) Single-label Problem transformation algorithm adaptation Ensemble

来源：评论

学校读者我要写书评

暂无评论

Automatic Multilabel Classification for Indonesian News Articles 2

Automatic Multilabel Classification for Indonesian News Arti...

引用

2nd International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA)

作者： Rahmawati, Dyah Khodra, Masayu Leylia Inst Teknol Bandun Sch Elect Engn & Informat Bandung Indonesia

ISBN: (纸本)9781467381437

Problem transformation and algorithm adaptation are the two main approaches in machine learning to solve multilabel classification problem. The purpose of this paper is to investigate both approaches in multilabel classification for Indonesian news articles. Since this classification deals with a large number of features, we also employ some feature selection methods to reduce feature dimension. There are four factors as the focuses of this paper, i. e., feature weighting method, feature selection method, multilabel classification approach, and singlelabel classification algorithm. These factors will be combined to determine the best combination. The experiments show that the best performer for multilabel classification of Indonesian news articles is the combination of TF-IDF feature weighting method, Symmetrical Uncertainty feature selection method, Calibrated Label Ranking -which belongs to problem transformation approach-, and SVM algorithm. This best combination achieves F-measure of 85.13% in 10-fold cross-validation, but the Fmeasure decreases to 76.73% in testing because of OOV.

关键词： multilabel classification problem transformation algorithm adaptation feature selection Indonesian news articles

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：