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Identification of influential observations based on binary particle swarm optimization in the cox PH model

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2020年第3期49卷 567-590页

作者： Sancar, Nuriye Inan, Deniz Near East Univ Dept Math CY-99138 Nicosia Cyprus Marmara Univ Dept Stat Istanbul Turkey

Proper identification of influential observations should be an integral and significant part of the Cox modeling process. This is because the failure to identify influential observations may have negative effect on the estimates acquired from the process. Furthermore, in survival analysis, influential observations frequently propose that the model has imperfections, including poorly specified factor, non-proportional hazards, loss of important information and/or a variable that has been omitted. Nevertheless, many procedures have been developed for the identification of a single influential observation based on the leave-one-out method. However, the results from leave-one-out diagnostic techniques are often misleading as a result of swamping and masking problems in the presence of multiple influential observations in the dataset. In this paper, identification of the optimal set of influential observations problem has been considered as the combinatorial optimization problem and a new simultaneous approach for identification of the optimal set of influential observations is proposed based on binary particle swarm optimization (BPSO) approach in the Cox PH model. The performance of the proposed BPSO-based approach and conventional diagnostic techniques have been compared according to various evaluation criteria by simulation studies. The performance of the BPSO-based approach has been also demonstrated by the clinical real dataset.

关键词： Cox PH Model Influential Observations binary particle swarm optimization Likelihood Displacement Masking Effect Swamping Effect

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A Modified binary particle swarm optimization for Knapsack Problems

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APPLIED MATHEMATICS AND COMPUTATION 2012年第22期218卷 11042-11061页

作者： Bansal, Jagdish Chand Deep, Kusum ABV Indian Inst Informat Technol & Management Gwa Gwalior 474010 India Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttar Pradesh India

The Knapsack Problems (KPs) are classical NP-hard problems in Operations Research having a number of engineering applications. Several traditional as well as population based search algorithms are available in literature for the solution of these problems. In this paper, a new Modified binary particle swarm optimization (MBPSO) algorithm is proposed for solving KPs, particularly 0-1 Knapsack Problem (KP) and Multidimensional Knapsack Problem (MKP). Compared to the basic binary particle swarm optimization (BPSO), this improved algorithm introduces a new probability function which maintains the diversity in the swarm and makes it more explorative, effective and efficient in solving KPs. MBPSO is tested through computational experiments over benchmark problems and the results are compared with those of BPSO and a relatively recent modified version of BPSO namely Genotype-Phenotype Modified binary particle swarm optimization (GPMBPSO). To validate our idea and demonstrate the efficiency of the proposed algorithm for KPs, experiments are carried out with various data instances of KP and MKP and the results are compared with those of BPSO and GPMBPSO. (C) 2012 Elsevier Inc. All rights reserved.

关键词： binary particle swarm optimization Knapsack Problems Sigmoid function

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Multi-modal aggression identification using Convolutional Neural Network and binary particle swarm optimization

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 118卷 187-197页

作者： Kumari, Kirti Singh, Jyoti Prakash Dwivedi, Yogesh K. Rana, Nripendra P. Natl Inst Technol Patna Patna Bihar India Swansea Univ Sch Management Bay Campus Swansea W Glam Wales Univ Bradford Sch Management Bradford W Yorkshire England

Aggressive posts containing symbolic and offencive images, inappropriate gestures along with provocative textual comments are growing exponentially in social media with the availability of inexpensive data services. These posts have numerous negative impacts on the reader and need an immediate technical solution to filter out aggressive comments. This paper presents a model based on a Convolutional Neural Network (CNN) and binary particle swarm optimization (BPSO) to classify the social media posts containing images with associated textual comments into non-aggressive, medium-aggressive and high-aggressive classes. A dataset containing symbolic images and the corresponding textual comments was created to validate the proposed model. The framework employs a pre-trained VGG-16 to extract the image features and a three-layered CNN to extract the textual features in parallel. The hybrid feature set obtained by concatenating the image and the text features were optimized using the BPSO algorithm to extract the more relevant features. The proposed model with optimized features and Random Forest classifier achieves a weighted F1-Score of 0.74, an improvement of around 3% over unoptimized features. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Cyber-aggression Cyberbullying Multi-modal data Convolutional Neural network binary particle swarm optimization

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Modified binary particle swarm optimization

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Progress in Natural Science:Materials International 2008年第9期18卷 1161-1166页

作者： Sangwook Lee Sangmoon Soak Sanghoun Oh Witold Pedrycz Moongu Jeon College of Computing Georgia Institute of Technology Atlanta GA 30332 USA Information Systems Examination Team Korean Intellectual Property Ofice (KIPO) Government Complex Daejeon Building 4 920 Dunsandong Seogu Republic of Korea Department of Information and Communications Gwangju Institute of Science and Technology 261 Cheomdan-gwagiro Buk-gu Gwanju Republic of Korea Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada T6G 2V4

This paper presents a modified binary particle swarm optimization (BPSO) which adopts concepts of the genotype-phenotype rep-resentation and the mutation operator of genetic *** main feature is that the BPSO can be treated as a continuous *** proposed BPSO algorithm is tested on various benchmark functions, and its performance is compared with that of the original *** results show that the modified BPSO outperforms the original BPSO algorithm.

关键词： binary particle swarm optimization Genotype-phenotype Mutation

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A modified binary particle swarm optimization with a machine learning algorithm and molecular docking for QSAR modelling of cholinesterase inhibitors

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SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021年第9期32卷 745-767页

作者： Shamsi, E. Rahati, A. Dehghanian, E. Univ Sistan & Baluchestan Fac Math Dept Comp Sci Zahedan Iran Univ Sistan & Baluchestan Fac Sci Dept Chem Zahedan Iran

The acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibitors play a key role in treating Alzheimer's disease. This study proposes an approach that integrates a modified binary particle swarm optimization (PSO) with a machine learning algorithm for building QSAR models to predict the activity of inhibitors for AChE and BuChE enzymes. More precisely, it uses a transfer function to convert the continuous search space of PSO to binary. Furthermore, it utilizes the concepts of catfish effect and chaotic map to improve exploration ability in searching for an optimum subset of descriptors for QSAR model constructions. Then, through a statistical method, it employs a machine learning algorithm to evaluate the fitness value of each candidate subset of features. Different combinations of four transfer functions with four machine learning algorithms, including K-nearest neighbour, multiple linear regression, support vector machine, and regression tree, were used to build several variants of the proposed algorithm. QSAR models constructed by each version were verified by internal and external validations. The best variants were selected based on a method called sum of ranking differences.

关键词： QSAR binary particle swarm optimization K-nearest neighbour multiple linear regression support vector machine regression tree

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An Enhanced binary particle swarm optimization for Optimal Feature Selection in Bearing Fault Diagnosis of Electrical Machines

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IEEE ACCESS 2021年 9卷 102671-102686页

作者： Lee, Chun-Yao Le, Truong-An Chung Yuan Christian Univ Dept Elect Engn Taoyuan 320314 Taiwan

This study proposes an effective bearing fault diagnosis model based on an optimized approach for feature selection. The measured signal of the electric motor is processed by envelope analysis and Hilbert-Huang transform techniques to extract the potential features. An enhancement of the binary particle swarm optimization algorithm through population initialization strategy based on feature weights, new updating mechanism, and the screening and replacing process create a new and effective feature selection method that improves classification accuracy and reduces data size. The optimal feature subset is provided separately for artificial neural networks, and support vector machine classifier for the final recognition task. In multiple case studies, the proposed feature selection method is evaluated against the benchmark data sets and shows performance comparable to that of other peer competitors. The effectiveness of the proposed bearing fault diagnosis model is verified on different testbeds and achieves high accuracy and robustness under noise conditions. In addition, experimental results are compared with existing fault diagnostic models, showing the high possibility of the proposed bearing fault diagnosis model.

关键词： Feature extraction Fault diagnosis Classification algorithms Task analysis Transforms particle swarm optimization Deep learning Bearing fault diagnosis feature extraction feature selection binary particle swarm optimization

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Spectrum Sensing for Cognitive Radio Using binary particle swarm optimization

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WIRELESS PERSONAL COMMUNICATIONS 2013年第4期72卷 2143-2153页

作者： Taha, Mohamed A. al Nadi, Dia I. Abu Princess Sumaya Univ Technol Dept Commun Engn Amman Jordan Univ Jordan Dept Elect Engn Amman Jordan

Spectrum sensing techniques in cognitive radio are the most important issue to exploit the spectrum efficiently. Several techniques have been proposed recently to estimate the dimension of the received signal from which the vacant frequencies can be determined and made available to the secondary users. These techniques have difficulties in low signal to noise ratio and limited sensing interval cases. It is known that the Maximum Likelihood Estimation (MLE) has an outstanding performance in most practical scenarios. In this paper, we present a Maximum Likelihood Estimate (MLE) to detect the number of vacant channels in the spectrum. The resulting MLE estimate posses several minima and maxima, therefore it needs exhaustive search to be determined accurately. To solve the problem, an evolutionary algorithm called binary particle swarm optimization (BPSO) is proposed. Simulation results have shown significant improvement of the MLE-BPSO estimate over the conventional techniques by 3-5 dB.

关键词： Cognitive radio Spectrum sensing Maximum likelihood estimation binary particle swarm optimization

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Instance selection for time series classification based on immune binary particle swarm optimization

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KNOWLEDGE-BASED SYSTEMS 2013年 49卷 106-115页

作者： Zhai, Tingting He, Zhenfeng Fuzhou Univ Dept Math & Comp Sci Fuzhou 350002 Fujian Peoples R China

We propose a new immune binary particle swarm optimization algorithm (IBPSO) to solve the problem of instance selection for time series classification, whose objective is to find out the smallest instance combination with maximal classification accuracy. The proposed IBPSO is based on the basic binary particle swarm optimization (BPSO) algorithm proposed by Kennedy and Eberhart. Its immune mechanism includes vaccination and immune selection. Vaccination employs the hubness score of time series and the particles' inertance as heuristic information to direct the search process. Immune selection procedure always discards the particle with the worst fitness in the current swarm for preventing the degradation of the swarm. Experimental results on small and medium datasets show that IBPSO outperforms BPSO and deterministic INSIGHT in terms of storage requirement and classification accuracy, and presents better robustness to noise than BPSO. In addition, experimental results on larger datasets indicate that IBPSO has better scalability than BPSO. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Instance selection Time series classification binary particle swarm optimization Immune algorithm Data reduction

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Optimal Design of Sparse Array for Ultrasonic Total Focusing Method by binary particle swarm optimization

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IEEE ACCESS 2020年 8卷 111945-111953页

作者： Zhang, Han Bai, Bichao Zheng, Jianfeng Zhou, Yun Chinese Acad Sci Inst Acoust Key Lab Noise & Vibrat Beijing 100190 Peoples R China Changzhou Univ Sch Mech Engn Changzhou 213164 Peoples R China

Ultrasonic phased array technology is used in various fields. Traditional full phased arrays place elements in every position of a uniform lattice with half-wavelength spacing between the lattice points, so the hardware cost is very high. This paper introduces an automatically method to sparsify the full array method with well-controlled sidelobes and the main lobe. By calculating one-dimensional phased array patterns that can reflect phased array performance, the binary particle swarm optimization (BPSO) algorithm is used to optimize the array layout. The method initialized form full array and decreased several elements step by step, then, a sparse array with comprehensive acoustic performance close to the reference full array is obtained. By applying the proposed method to the sparse array design of total focusing method (TFM), the simulation results indicate that the proposed sparse total focusing method can greatly increase computational efficiency while providing significantly higher image quality. The BPSO can provide effective optimization design for sparse arrays.

关键词： Phased arrays Acoustics Arrays Acoustic beams Ultrasonic transducer arrays Focusing binary particle swarm optimization sparse array total focusing method ultrasonic phased array

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Improved contourlet-based steganalysis using binary particle swarm optimization and radial basis neural networks

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NEURAL COMPUTING & APPLICATIONS 2012年第7期21卷 1717-1728页

作者： Sheikhan, Mansour Pezhmanpour, Mansoureh Moin, M. Shahram Islamic Azad Univ S Tehran Branch EE Dept Fac Engn Tehran Iran Iran Telecom Res Ctr IT Dept Multimedia Syst Grp Tehran Iran

Steganography is the science of hiding information in a media such as video, image or audio files. On the other hand, the aim of steganalysis is to detect the presence of embedded data in a given media. In this paper, a steganalysis method is presented for the colored joint photographic experts group images in which the statistical moments of contourlet transform coefficients are used as the features. In this way, binary particle swarm optimization algorithm is also employed as a closed-loop feature selection method to select the efficient features in tandem with improvement of the detection rate. Nonlinear support vector machine and two variants of radial basis neural networks, i.e., radial basis function and probabilistic neural network, are used as the classification tools and their performance is compared in detecting the stego and clean images. Experimental results show that even for low embedding rates, the detection accuracy of the proposed method is more than 80% along with 30% reduction in the size of feature set.

关键词： Steganalysis Contourlet transform binary particle swarm optimization Radial basis neural networks Support vector machine

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