High performance genetic algorithm based text clustering using parts of speech and outlier elimination

Shi, Kansheng; Le-ming, LI

doi:10.1007/s10489-012-0382-8

Cited by 24 publications

(15 citation statements)

References 13 publications

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“…Biologically motivated intelligent control is the discipline in which control algorithms are developed by emulating certain characteristics of intelligent biological systems. Many control techniques, such as artificial neural networks [4,5], fuzzy control [6,7], and genetic algorithms [8,9], had proven its effectiveness in solving wide range of complex control problems. Recently, a new member was added to this family of biologically motivated intelligent control, which mimics the emotional learning process in the limbic system of the mammalian brains.…”

Section: Introductionmentioning

confidence: 99%

Brain Emotional Learning Based Intelligent Decoupler for Nonlinear Multi‐Input Multi‐Output Distillation Columns

El-Saify

El-Garhy

El-Sheikh

2017

Mathematical Problems in Engineering

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The distillation process is vital in many fields of chemical industries, such as the two-coupled distillation columns that are usually highly nonlinear Multi-Input Multi-Output (MIMO) coupled processes. The control of MIMO process is usually implemented via a decentralized approach using a set of Single-Input Single-Output (SISO) loop controllers. Decoupling the MIMO process into group of single loops requires proper input-output pairing and development of decoupling compensator unit. This paper proposes a novel intelligent decoupling approach for MIMO processes based on new MIMO brain emotional learning architecture. A MIMO architecture of Brain Emotional Learning Based Intelligent Controller (BELBIC) is developed and applied as a decoupler for 4 input/4 output highly nonlinear coupled distillation columns process. Moreover, the performance of the proposed Brain Emotional Learning Based Intelligent Decoupler (BELBID) is enhanced using Particle Swarm Optimization (PSO) technique. The performance is compared with the PSO optimized steady state decoupling compensation matrix. Mathematical models of the distillation columns and the decouplers are built and tested in simulation environment by applying the same inputs. The results prove remarkable success of the BELBID in minimizing the loops interactions without degrading the output that every input has been paired with.

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Section: Introductionmentioning

confidence: 99%

Brain Emotional Learning Based Intelligent Decoupler for Nonlinear Multi‐Input Multi‐Output Distillation Columns

El-Saify

El-Garhy

El-Sheikh

2017

Mathematical Problems in Engineering

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“…The formula in equation (3) had adopted, for instance, by Shi and Li (2013) with minor modification to consider the document length on the impact of weight normalized to the interval [0,1]. Radwan et al (2006), computed the document weighting from the formula suggested by (Salton and Buckley, 1990) as follows: .…”

Section: Document Feature Representationmentioning

confidence: 99%

“…A patented document Clustering algorithm using GA Model (CGAM) was invented by Shi and Li (2013). It is a GA based k-means that also took into consideration the impact of the outliers and part of the speech.…”

Section: Content Clusteringmentioning

confidence: 99%

Advances in Document Clustering with Evolutionary-Based Algorithms

Makki¹,

Yaakob²,

Mustapha³

et al. 2015

American Journal of Applied Sciences

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Document clustering is the process of organizing a particular electronic corpus of documents into subgroups of similar text features. Formerly, a number of conventional algorithms had been applied to perform document clustering. There are current endeavors to enhance clustering performance by employing evolutionary algorithms. Thus, such endeavors became an emerging topic gaining more attention in recent years. The aim of this paper is to present an up-to-date and self-contained review fully devoted to document clustering via evolutionary algorithms. It firstly provides a comprehensive inspection to the document clustering model revealing its various components with its related concepts. Then it shows and analyzes the principle research work in this topic. Finally, it compiles and classifies various objective functions, the core of the evolutionary algorithms, from the related collection of research papers. The paper ends up by addressing some important issues and challenges that can be subject of future work.

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“…Majority of the text clustering paradigm employs bag-of-words approach, where each distinct term present in the documents collection is considered as a feature for the document's representation [5]. A data clustering is the divides the documents into two groups such as the closeness among the documents of the same gathering is augmented and the comparability among the data of various groups is minimized [6]. Document clustering idea is utilized as a part of different regions like data recovery, content mining and so on.…”

Section: Introductionmentioning

confidence: 99%

Text Categorization and graphical representation using Improved Markov Clustering

Somasekar¹,

Naveen²

2018

IJIES

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Abstract:Text categorization means dividing a set of input documents into the two or more classes to which these documents belong. Because of increase in availability of data in digital form in large amount, it becomes necessary to organize it. Feature extraction is the crucial step in text classification. Most of the existing text classifiers are lacking in finding out the relations among the terms. In this research work proposed text clustering of Improved Markov Clustering Model (IMCM) and text classification in which the nonlinear relations among the terms are also considered. This approach is scalable to huge dataset also and its classification power is not affected if relations among terms are large. The proposed IMCM method improved the text document clustering significantly and helped to avoid the human interaction in text clustering. Experimental results have shown that proposed system is 91% accurate for 8 categories and decreases, as increase the classes from 8 to 12, from 96% to 94%, respectively. Also, compared proposed model with existing Naive Bayes and k-Nearest Neighbor classifiers. Experimental results show that the proposed model is more accurate than these two classifiers. The better results demonstrated that presented system is developed effectively.

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High performance genetic algorithm based text clustering using parts of speech and outlier elimination

Cited by 24 publications

References 13 publications

Brain Emotional Learning Based Intelligent Decoupler for Nonlinear Multi‐Input Multi‐Output Distillation Columns

Brain Emotional Learning Based Intelligent Decoupler for Nonlinear Multi‐Input Multi‐Output Distillation Columns

Advances in Document Clustering with Evolutionary-Based Algorithms

Text Categorization and graphical representation using Improved Markov Clustering

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