An hybrid particle swarm optimization with crow search algorithm for feature selection

Adamu, A. K.; Mohammed, Abdullahi; Junaidu, Sahalu B.; Hassan, Ibrahim Hayatu

doi:10.1016/j.mlwa.2021.100108

Cited by 51 publications

(38 citation statements)

References 44 publications

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“…Several other types of research have presented the hybridization between various optimization techniques such as chaotic crow search and particle swarm optimization algorithm in Adamu et al. ( 2021 ), sine cosine algorithm and cuckoo search in Khamees and Al-Baset ( 2020 ), and Firefly algorithm and differential evolution (Zhang et al. 2016 ).…”

Section: Related Workmentioning

confidence: 99%

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ST-AL: a hybridized search based metaheuristic computational algorithm towards optimization of high dimensional industrial datasets

et al. 2022

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The rapid growth of data generated by several applications like engineering, biotechnology, energy, and others has become a crucial challenge in the high dimensional data mining. The large amounts of data, especially those with high dimensions, may contain many irrelevant, redundant, or noisy features, which may negatively affect the accuracy and efficiency of the industrial data mining process. Recently, several meta-heuristic optimization algorithms have been utilized to evolve feature selection techniques for dealing with the vast dimensionality problem. Despite optimization algorithms’ ability to find the near-optimal feature subset of the search space, they still face some global optimization challenges. This paper proposes an improved version of the sooty tern optimization (ST) algorithm, namely the ST-AL method, to improve the search performance for high-dimensional industrial optimization problems. ST-AL method is developed by boosting the performance of STOA by applying four strategies. The first strategy is the use of a control randomization parameters that ensure the balance between the exploration–exploitation stages during the search process; moreover, it avoids falling into local optimums. The second strategy entails the creation of a new exploration phase based on the Ant lion (AL) algorithm. The third strategy is improving the STOA exploitation phase by modifying the main equation of position updating. Finally, the greedy selection is used to ignore the poor generated population and keeps it from diverging from the existing promising regions. To evaluate the performance of the proposed ST-AL algorithm, it has been employed as a global optimization method to discover the optimal value of ten CEC2020 benchmark functions. Also, it has been applied as a feature selection approach on 16 benchmark datasets in the UCI repository and compared with seven well-known optimization feature selection methods. The experimental results reveal the superiority of the proposed algorithm in avoiding local minima and increasing the convergence rate. The experimental result are compared with state-of-the-art algorithms, i.e., ALO, STOA, PSO, GWO, HHO, MFO, and MPA and found that the mean accuracy achieved is in range 0.94–1.00.

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Section: Related Workmentioning

confidence: 99%

“…( 2021 ) Butterfly algorithm with PSO 2021 Assiri ( 2021 ) Butterfly algorithm, chaotic local search, and opposition-based learning 2021 Che and He ( 2021 ) Whale optimization with Seagull optimization algorithm 2021 Adamu et al. ( 2021 ) Chaotic crow search and PSO 2021 …”

Section: Related Workmentioning

confidence: 99%

ST-AL: a hybridized search based metaheuristic computational algorithm towards optimization of high dimensional industrial datasets

et al. 2022

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“…The CSA is a novel-inspired metaheuristic method proposed in 2016 and has resolved many complex optimization problems [51], with many researchers attempting to boost it from different aspects [52][53][54][55][56]. For example, Adamu et al [57] proposed a hybrid particle swarm optimization with the CSA for feature selection, and the result of the proposed model gained an accuracy of 89.67% on 15 datasets. Aliabadi et al [58] designed an improved CSA to optimize a hybrid renewable energy system in radial distribution networks, and the results showed significant active losses and voltage deviations.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

Zhang¹,

Yan

et al. 2022

Applied Sciences

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College students are the group with the most entrepreneurial vitality and potential. How to cultivate their entrepreneurial and innovative ability is one of the important and urgent issues facing this current social development. This paper proposes a reliable, intelligent prediction model of entrepreneurial intentions, providing theoretical support for guiding college students’ positive entrepreneurial intentions. The model mainly uses the improved crow search algorithm (CSA) to optimize the kernel extreme learning machine (KELM) model with feature selection (FS), namely CSA-KELM-FS, to study entrepreneurial intention. To obtain the best fitting model and key features, the gradient search rule, local escaping operator, and levy flight mutation (GLL) mechanism are introduced to enhance the CSA (GLLCSA), and FS is used to extract the key features. To verify the performance of the proposed GLLCSA, it is compared with eight other state-of-the-art methods. Further, the GLLCSA-KELM-FS model and five other machine learning methods have been used to predict the entrepreneurial intentions of 842 students from the Wenzhou Vocational College in Zhejiang, China, in the past five years. The results show that the proposed model can correctly predict the students’ entrepreneurial intention with an accuracy rate of 93.2% and excellent stability. According to the prediction results of the proposed model, the key factors affecting the student’s entrepreneurial intention are mainly the major studied, campus innovation, entrepreneurship practice experience, and positive personality. Therefore, the proposed GLLCSA-KELM-FS is expected to be an effective tool for predicting students’ entrepreneurial intentions.

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“…Dalam data mining, permasalahan akurasi merupakan permasalahan mendasar dalam penelitian [7]. Biasanya, dataset yang digunakan dalam klasifikasi berisikan data yang noise, redudansi data, serta memiliki atribut yang tidak berguna [8]. Untuk menangani ini dibutuhkannya seleksi atribut yang mengidentifikasi atribut yang relevan tanpa mengurangi akurasi dari algoritma itu sendiri [5].…”

Section: Pendahuluanunclassified

Perbandingan Algoritma C4.5 dengan C4.5+Particle Swarm Optimization untuk Klasifikasi Angkatan Kerja

Safira

Mustakim

2021

JKT

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Dalam suatu dataset yang besar, data mining merupakan sebuah proses penyelesaian yang menghasilkan beberapa pola baru menjadi pengetahuan yang berguna. Algoritma yang sering dipakai dalam machine learning salah satunya adalah C4.5. Algoritma ini terkenal sangat kuat dalam melakukan klasifikasi, namun masih memiliki beberapa kelemahan seperti sering terjadinya overlapping dan overfitting data yang membuat data menjadi tidak relevan sehingga dapat mengurangi tingkat akurasi dari algoritma. Untuk menangani ini dibutuhkannya seleksi atribut yang dapat mengidentifikasi atribut yang relevan, tanpa mengurangi akurasi dari algoritma itu sendiri. Algoritma optimasi Particle Swarm Optimization (PSO) merupakan salah satu algoritma yang mampu digunakan sebagai seleksi atribut. Keuntungan dari PSO ini mudah diterapkan, efisien dalam perhitungan dan memiliki konsep yang sederhana dibandingkan dengan teknik optimasi lainnya. Jumlah dataset yang akan digunakan pada penelitian ini sebanyak 2.518 dataset dimana sebelumnya dilakukan pembagian dataset dengan menggunakan algoritma K-Means dan K-Medoid. Atribut yang digunakan sebanyak 4 atribut yaitu, jenis kelamin, umur, tingkat produktivitas dan pendidikan terakhir. Dari penelitian ini, diperoleh hasil bahwa akurasi yang diberikan oleh C4.5 yang telah dioptimasi menggunakan algoritma Particle Swarm Optimization (PSO) terbukti lebih tinggi dibandingkan mengggunakan algoritma C4.5 saja. Dimana algoritma C4.5+PSO memiliki akurasi sebesar 66,80% sedangkan algoritma C4.5 memiliki akurasi sebesar 76,32%.

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An hybrid particle swarm optimization with crow search algorithm for feature selection

Cited by 51 publications

References 44 publications

ST-AL: a hybridized search based metaheuristic computational algorithm towards optimization of high dimensional industrial datasets

ST-AL: a hybridized search based metaheuristic computational algorithm towards optimization of high dimensional industrial datasets

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

Perbandingan Algoritma C4.5 dengan C4.5+Particle Swarm Optimization untuk Klasifikasi Angkatan Kerja

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