A new approach to minimum attribute reduction based on discrete artificial bee colony

Dong-yi, YE; Chen, Zhaojiong

doi:10.1007/s00500-014-1371-0

Cited by 28 publications

(7 citation statements)

References 37 publications

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“…It is known from the experiment result that the interactive marketing can significantly grasp customer demand and attract customer's attention, which is currently the most dominant focus in the field of marketing [18]. Therefore, in order to set up an active communication channel with the customer and to enhance the customer's purchase desire and loyalty with a live conversation so the outlet can be more active to sell products to existing customers, the software agent mechanism is introduced, which will be able to avoid dull interaction with the customer and provide the customer with new shopping experience via a new rendering interface [39]- [41].…”

Section: B Interactive System Environment Based On the Software Agentmentioning

confidence: 99%

“…From the definition and concept of the sensor service proposed by the scholars [26]- [28], its structure can be divided into three layers. One of them is the "Sensor Component", which is composed of a variety of information-retrieving and identifying components and is responsible for access to the behavior patterns of objects; the middle layer is the Middleware, which is composed of various types of wireless transmission technology and is responsible for the decoding, conversion and debugging of the data; the back-end is the "Application Interface", which is composed of a variety of applications and services based on the sensor components, such as environment checking and testing, traffic management and security [41,42]. To make this system operate smoothly, there will exist an Enterprise Service Platform which is responsible for connecting various transmission networks and application services, as shown in Figure 2.…”

Section: Sensor Servicementioning

confidence: 99%

See 1 more Smart Citation

Intelligent Agent-Based Predict System With Cloud Computing for Enterprise Service Platform in IoT Environment

Yang¹,

Chen

et al. 2021

IEEE Access

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Section: B Interactive System Environment Based On the Software Agentmentioning

confidence: 99%

Section: Sensor Servicementioning

confidence: 99%

Intelligent Agent-Based Predict System With Cloud Computing for Enterprise Service Platform in IoT Environment

Yang¹,

Chen

et al. 2021

IEEE Access

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“…Seeking solutions for discrete optimisation problems with the help of the ABC algorithm is an area of interest that is constantly increasing. One such discrete optimisation problem is the workplace flow scheduling problem, for which models for dynamic clustering processes in different types of datasets have been presented [20][21][22][23][24][25]. In addition, next-generation problems such as cloud service composition, DNA sequencing, and DNA three-dimensional structure prediction problems are present in the literature [26][27][28].…”

Section: Artificial Bee Colony (Abc) Algorithm For Aspmentioning

confidence: 99%

Optimum Assembly Sequence Planning System Using Discrete Artificial Bee Colony Algorithm

Özmen

Batbat

Özen

et al. 2018

Mathematical Problems in Engineering

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Assembly refers both to the process of combining parts to create a structure and to the product resulting therefrom. The complexity of this process increases with the number of pieces in the assembly. This paper presents the assembly planning system design (APSD) program, a computer program developed based on a matrix-based approach and the discrete artificial bee colony (DABC) algorithm, which determines the optimum assembly sequence among numerous feasible assembly sequences (FAS). Specifically, the assembly sequences of three-dimensional (3D) parts prepared in the computer-aided design (CAD) software AutoCAD are first coded using the matrix-based methodology and the resulting FAS are assessed and the optimum assembly sequence is selected according to the assembly time optimisation criterion using DABC. The results of comparison of the performance of the proposed method with other methods proposed in the literature verify its superiority in finding the sequence with the lowest overall time. Further, examination of the results of application of APSD to assemblies consisting of parts in different numbers and shapes shows that it can select the optimum sequence from among hundreds of FAS.

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“…As a feature dimensionality reduction method in machine learning and deep learning, unsupervised learning aims to extract useful feature information from unlabeled data which not only can be directly ap-plied to the recognition, classification and prediction system, but also can provide initial training values for supervised learning [4,23,46]. Although currently like deep convolutional neural networks (CNNs) Information Technology and Control 2020/3/49 422 such a supervised learning method has achieved good results in the application [22], but whether its performance is good or bad depends on the amount of marked training sample [15,19,24,27], and collecting and marking these data is very difficult [32,47].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Text Feature Learning via Deep Variational Auto-encoder

Liu

Xie

Chen

2020

ITC

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Dimensionality reduction plays an important role in the data processing of machine learning and data mining, which makes the processing of high-dimensional data more efficient. Dimensionality reduction can extract the low-dimensional feature representation of high-dimensional data, and an effective dimensionality reduction method can not only extract most of the useful information of the original data, but also realize the function of removing useless noise. The dimensionality reduction methods can be applied to all types of data, especially image data. Although the supervised learning method has achieved good results in the application of dimensionality reduction, its performance depends on the number of labeled training samples. With the growing of information from internet, marking the data requires more resources and is more difficult. Therefore, using unsupervised learning to learn the feature of data has extremely important research value. In this paper, an unsupervised multilayered variational auto-encoder model is studied in the text data, so that the high-dimensional feature to the low-dimensional feature becomes efficient and the low-dimensional feature can retain mainly information as much as possible. Low-dimensional feature obtained by different dimensionality reduction methods are used to compare with the dimensionality reduction results of variational auto-encoder (VAE), and the method can be significantly improved over other comparison methods.

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A new approach to minimum attribute reduction based on discrete artificial bee colony

Cited by 28 publications

References 37 publications

Intelligent Agent-Based Predict System With Cloud Computing for Enterprise Service Platform in IoT Environment

Intelligent Agent-Based Predict System With Cloud Computing for Enterprise Service Platform in IoT Environment

Optimum Assembly Sequence Planning System Using Discrete Artificial Bee Colony Algorithm

Unsupervised Text Feature Learning via Deep Variational Auto-encoder

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