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Optimizing beyond boundaries: empowering the salp swarm algorithm for global optimization and defective software module classification

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Neural computing and Applications 2024年第30期36卷 18727-18759页

作者： Kassaymeh, Sofian Al-Betar, Mohammed Azmi Rjoubd, Gaith Fraihat, Salam Abdullah, Salwani Almasri, Ammar Software Engineering Department Faculty of Information Technology Aqaba University of Technology Aqaba Jordan College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Artificial Intelligence Department Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Data Mining and Optimization Research Group Center for Artificial Intelligence Technology Universiti Kebangsaan Malaysia Bangi Malaysia Management Information Systems Department Al-Balqa Applied University Amman Jordan

This work presents a new version of the salp swarm optimizer (SSA), called "mSSA," that uses complex mathematical expressions to dynamically manipulate the crucial control parameter (c1) during optimization. These expressions are carefully designed to modulate the shift in search strategy from exploratory to exploitative, improving the flexibility and speed of convergence of the algorithm. To evaluate the performance of the developed mSSA variants, a thorough examination is carried out on twenty-three benchmark test functions alongside their application to the complex task of software module classification. The process of classifying defective software modules involves developing a multilayer perceptron (MLP) classifier that is suited to the particular complexity and heterogeneity of the task. Selecting the best optimizer is made easier by systematically evaluating the different mSSA versions as MLP classifier trainers. Based on metrics like classification accuracy, convergence speed, and avoidance of local minima, a comparative analysis in opposition to six previously published metaheuristic optimizers shows that mSSA3, when combined with the developed MLP classifier, outperforms both other mSSA variations and state-of-the-art metaheuristic optimizers in terms of overall performance. The excellent classification accuracy, swift convergence, and ability to avoid local minima of mSSA3 highlight its superiority and establish it as a cutting-edge method in the application of metaheuristic algorithms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Global optimization

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Discovering Bayesian market views for intelligent asset allocation

arXiv

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arXiv 2018年

作者： Xing, Frank Z. cambria, Erik Malandri, Lorenzo Vercellis, carlo School of Computer Science and Engineering Nanyang Technological University Data Mining and Optimization Research Group Politecnico di Milano

Along with the advance of opinion mining techniques, public mood has been found to be a key element for stock market prediction. However, how market participants' behavior is affected by public mood has been rarely discussed. consequently, there has been little progress in leveraging public mood for the asset allocation problem, which is preferred in a trusted and interpretable way. In order to address the issue of incorporating public mood analyzed from social media, we propose to formalize public mood into market views, because market views can be integrated into the modern portfolio theory. In our framework, the optimal market views will maximize returns in each period with a Bayesian asset allocation model. We train two neural models to generate the market views, and benchmark the model performance on other popular asset allocation strategies. Our experimental results suggest that the formalization of market views significantly increases the profitability (5% to 10% annually) of the simulated portfolio at a given risk level. copyright © 2018, The Authors. All rights reserved.

关键词： Investments

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Skipping Strategy (SS) for Initial Population of Job-Shop Scheduling Problem

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Journal of Physics: conference Series 2018年第1期978卷

作者： M Abdolrazzagh-Nezhad E B Nababan H M Sarim Department of Computer Engineering Faculty of Engineering Bozorgmehr University of Qaenat Qaen Iran. Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Data Mining and Optimization Research Group Center for Artificial Intelligence Technology Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor Malaysia

Initial population in job-shop scheduling problem (JSSP) is an essential step to obtain near optimal solution. Techniques used to solve JSSP are computationally demanding. Skipping strategy (SS) is employed to acquire initial population after sequence of job on machine and sequence of operations (expressed in Plates-jobs and mPlates-jobs) are determined. The proposed technique is applied to benchmark datasets and the results are compared to that of other initialization techniques. It is shown that the initial population obtained from the SS approach could generate optimal solution.

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The analysis for Gait Energy Image based on statistical methods

The analysis for Gait Energy Image based on statistical meth...

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2016 International conference on Advances in Electrical, Electronic and Systems Engineering, IcAEES 2016

作者： Rahman, Siti Zaharah Abd Abdullah, Siti Norul Huda Sheikh Nazri, Mohd Zakree Bin Ahmad Pattern Recognition Research Group Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Data Mining and Optimization Group Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia

ISBN: (纸本)9781509028894

Gait features representation is an important step in gait biometric recognition. It represents content of the individual image and the pattern of human walking simultaneously. Discriminative features can provide the most relevant and useful information for individual classification. Many state of the art feature fusions based on average silhouette were used for gait feature representation technique. Those fusion methods are mostly robust in simple case but inadequate for difficult gait representation such as walking with occlusion, walking with different view and walking with briefcase that affect the accuracy of gait performance. For that reason, we are exploring and proposing another potential feature fusion based on statistical method namely edges directed matrix of Gait Energy Image (EDMSGEI). We compare our EDMSGEI method with Gray Level co-Occurrence Matrix (GLcMGEI) and Histogram of Gaussian (HOGGEI) method for individual recognition with K-Nearest neighbour classifier. The datasets involved are cASIA and UKM-cSM dataset images. Apart from HOGGEI, this study also shows that EDMSGEI gained better gait recognition performance. © 2016 IEEE.

关键词： Statistical methods

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Arabic Sentiment Analysis with Optimal combination of Features Selection and Machine Learning Approaches

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research Journal of Applied Sciences, Engineering and Technology 2016年第5期13卷 386-393页

作者： Bilal Sabri Saidah Saad Data Mining and Optimization Research Group (DMO) Centre for Artificial Intelligence Technology (CAIT) Faculty of Information Science and Technologi School of Computer Science Universiti Kebangsaan Malaysia (UKM) 43600 Bandar Baru Bangi Malaysia

The main objective of this research study is to design a model that allows for the utilization of a novel technique for the implementation of sentiment analysis in the Arabic language. Sentiment analysis is an interesting task that includes web mining, Natural Language Processing (NLP) and Machine Learning (ML). Most of the research work on sentiment analysis was focused on the texts in the English language. Therefore, the research on sentiment analysis in the Arabic language and other languages are in the infancy stage. This study empirically evaluates three Feature Selection Methods (FSM) (Information Gain (IG), chi-square (cHI) and Gini Index (GI)) and, three classification approaches (Association Rule (AR) mining and the N-gram model and the Meta-classifier approach) for the implementation of sentiment classification in the Arabic language. A number of related experiments have been carried out on the Opinion corpus of Arabic (OcA). The results obtained from the experiments were favorable, depending on the algorithms used and the number of selected feature has proven that the use of FS method can increase the performance of sentiment classification in the Arabic language. The results of the experiments reveal that FS method is obtained to develop the classifier performance. Furthermore, the results of the experiment indicated that the use of cHI feature selection has produced the best performance for FS and the performance of meta-classifier a combination approach has outperformed the other approaches for sentiment classification in the Arabic language. In conclusion, this research study has proven that the combination approach (meta-classifier) with the chi-square FS method produces the most accurate classification technique, as high as 90.80%.

关键词： Feature selection meta-classifier machine learning approach NLP opinion mining sentiment analysis

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Incremental-Eclat model: An implementation via benchmark case study 1

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International conference on Machine Learning and Signal Processing, MALSIP 2015

作者： Bakar, Wan Aezwani Bt Wan Abu Abdullah, Zailani B. Saman, Md. Yazid B. Md Jalil, Masita@Masila Bt Abd Man, Mustafa B. Herawan, Tutut Hamdan, Abdul Razak Department of Computer Science School of Informatics and Applied Mathematics Universiti Malaysia Terengganu Kuala TerengganuTerengganu21030 Malaysia Department of Information Systems Faculty of Computer Science and Information Technology University of Malaya Lembah Pantai Kuala Lumpur50603 Malaysia Data Mining and Optimization Research Group Fakulti Teknologi & Sains Maklumat Universiti Kebangsaan Malaysia BangiSelangor43650 Malaysia

ISBN: (数字)9783319322131

ISBN: (纸本)9783319322124

Association Rule mining (ARM) is one of the most prominent areas in detecting pattern analysis especially for crucial business decision making. With the aims to extract interesting correlations, frequent patterns, association or casual structures among set of items in the transaction databases or other data repositories, the end product of association rule mining is the analysis of pattern that could be a major contributor especially in managerial decision making. Most of previous frequent mining techniques are dealing with horizontal format of their data repositories. However, the current and emerging trend exists where some of the research works are focusing on dealing with vertical data format and the rule mining results are quite promising. One example of vertical rule mining technique is called Eclat which is the abbreviation of Equivalence class Transformation. © Springer International Publishing Switzerland 2016.

关键词： Equivalence classes

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Statistical analysis for validating AcO-KNN algorithm as feature selection in sentiment analysis

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AIP conference Proceedings 2017年第1期1891卷

作者： Siti Rohaidah Ahmad Nurhafizah Moziyana Mohd Yusop Azuraliza Abu Bakar Mohd Ridzwan Yaakub 1Department of Computer Science Faculty of Defence Science & Technology Universiti Pertahanan Nasional Malaysia 57000 Kuala Lumpur Malaysia 2Data Mining and Optimization Research Group Centre for Artificial Intelligence Technology Faculty of Information Science & Technology Universiti Kebangsaan Malaysia 46000 Bangi Selangor Malaysia

This research paper aims to propose a hybrid of ant colony optimization (AcO) and k-nearest neighbor (KNN) algorithms as feature selections for selecting and choosing relevant features from customer review datasets. Information gain (IG), genetic algorithm (GA), and rough set attribute reduction (RSAR) were used as baseline algorithms in a performance comparison with the proposed algorithm. This paper will also discuss the significance test, which was used to evaluate the performance differences between the AcO-KNN, IG-GA, and IG-RSAR algorithms. This study evaluated the performance of the AcO-KNN algorithm using precision, recall, and F-score, which were validated using the parametric statistical significance tests. The evaluation process has statistically proven that this AcO-KNN algorithm has been significantly improved compared to the baseline algorithms. The evaluation process has statistically proven that this AcO-KNN algorithm has been significantly improved compared to the baseline algorithms. In addition, the experimental results have proven that the AcO-KNN can be used as a feature selection technique in sentiment analysis to obtain quality, optimal feature subset that can represent the actual data in customer review data.

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Metaheuristic algorithms for feature selection in sentiment analysis

Metaheuristic algorithms for feature selection in sentiment ...

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Science and Information conference, SAI 2015

作者： Ahmad, Siti Rohaidah Bakar, Azuraliza Abu Yaakub, Mohd Ridzwan Department of Science Computer Faculty of Defence Science and Technology Universiti Pertahanan Nasional Malaysia Sungai Besi Kuala Lumpur Malaysia Data Mining and Optimization Research Group Centre for Artificial Intelligence Technology Faculty of Information Science and Technology University Kebangsaan Malaysia Selangor Malaysia

ISBN: (纸本)9781479985470

Sentiment analysis functions by analyzing and extracting opinions from documents, websites, blogs, discussion forums and others to identify sentiment patterns on opinions expressed by consumers. It analyzes people's sentiment and identifies types of sentiment in comments expressed by consumers on certain matters. This paper highlights comparative studies on the types of feature selection in sentiment analysis based on natural language processing and modern methods such as Genetic Algorithm and Rough Set Theory. This study compares feature selection in text classification based on traditional and sentiment analysis methods. Feature selection is an important step in sentiment analysis because a suitable feature selection can identify the actual product features criticized or discussed by consumers. It can be concluded that metaheuristic based algorithms have the potential to be implemented in sentiment analysis research and can produce an optimal subset of features by eliminating features that are irrelevant and redundant. © 2015 IEEE.

关键词： Sentiment analysis

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Metaheuristic algorithms for feature selection in sentiment analysis

Metaheuristic algorithms for feature selection in sentiment ...

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Science and Information conference (SAI)

作者： Siti Rohaidah Ahmad Azuraliza Abu Bakar Mohd Ridzwan Yaakub Department of Science Computer Universiti Pertahanan Nasional Malaysia Kuala Lumpur Malaysia Data Mining and Optimization Research Group University Kebangsaan Malaysia Selangor Malaysia

关键词： Feature extraction Sentiment analysis Genetic algorithms Text categorization classification algorithms Accuracy

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Smart Root Search (SRS): A New Search Algorithm to Investigate combinatorial Problems

Smart Root Search (SRS): A New Search Algorithm to Investiga...

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International conference on computational Intelligence, Modelling and Simulation (cSSIM)

作者： Narjes Khatoon Naseri Elankovan Sundararajan Masri Ayob Amin Jula Universiti Kebangsaan Malaysia Bangi Selangor MY Faculty of Information Science and Technology Universiti Kebangsaan Malaysia UKM Bangi Selangor Malaysia Data Mining and Optimization Research Group (DMO) Universiti Kebangsaan Malaysia UKM Bangi Selangor Malaysia

ISBN: (纸本)9781467380843

In recent years researchers have tried to apply Stochastic Algorithms for solving optimization problems. Some of these algorithms like Genetic Algorithm (GA), Ant colony optimization (AcO), Particle Swarm optimization (PSO) and Artificial Immune Systems (AIS) are more known because of their significant abilities in finding optimal solutions of the problems comparing to others. Although these algorithms show many advantages in solving optimization problems, they face some drawbacks affect on their performance. Local optima issues and lack of local search capability are two obvious weaknesses that each of the algorithms confronts at least one. To cope these challenges, in this study, plants' root growth intelligences will be inspired in proposing a novel nature-inspired optimization algorithm called Smart Root Search (SRS). The SRS simulates the extracted intelligent behaviors of plant roots in finding nutrition and water in soil. The proposed algorithm will provide embedded exploitation mechanisms besides quick and effective exploring features for escaping of the trap of local optima, and uses problem-space division. Using partitioned problem search space not only enhances ability of the algorithm to avoid of local optima, but makes it a scalable and flexible method in performing balanced search activities. The algorithm will further demonstrate its high-performance search ability in confronting high-dimensional search-space problems.

关键词： Search problems optimization Algorithm design and analysis Heuristic algorithms classification algorithms Hair Soil

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