Faculty Profiles - KITA, Eisuke

Papers - KITA, Eisuke

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Stochastic Schemata Exploiter-Based AutoML Reviewed

Makino, H; Kita, E

21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS ICDMW 2021 Vol. 2021-December page： 238 - 245 2021

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：IEEE International Conference on Data Mining Workshops, ICDMW

Machine Learning (ML) models are often determined by relying on knowledge and experience. In recent years, many automatic building methods are proposed, but there are some problems related to accuracy, computation cost, and explainability. We propose the Stochastic Schemata Exploiter-Based AutoML. The Stochastic Schemata Exploiter (SSE) is one of the Evolutionary Algorithms. In each generation, SSE calculates fitness of the individuals, defines the best subsets according to their average fitness, generates individuals based on the subsets, and applies a mutation operator. Since the original SSE uses a binary string representation, we have to modify the SSE algorithm in the following points for parameter optimization: the initialization method, the schema extraction method, the new individual generation method, the mutation method, and the generation update method. In this paper, we propose a genetic representation of the stacking model and optimize the stacking model using SSE. Compared with the Genetic Algorithm, the Tree-structured Parzen Estimator, the Covariance Matrix Adaptation-Evolution Strategy, and Random Search, SSE shows an interesting feature: a better accuracy for combinatorial optimization problems with categorical, discrete, and continuous variables, such as hyper-parameter optimization. In addition, we propose the visualization of the process of SSE. The visualization helps us to understand the process, which is another advantage of the SSE-based optimization (SSEopt).

DOI： 10.1109/ICDMW53433.2021.00037

Web of Science

Scopus

Control Program Design of Autonomous Vehicle Robot Using Grammatical Evolution Reviewed

Yamada S., Sato R., Tamaki T., Kita E.

2021 6th International Conference on Robotics and Automation Engineering, ICRAE 2021 page： 308 - 312 2021

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：2021 6th International Conference on Robotics and Automation Engineering, ICRAE 2021

The automatic design of the control program of the vehicle robot is presented in this study. LEGO MINDSTORM EV3 and RVW Level Builder are used for this study. The control program of EV3 is designed automatically by using Grammatical Evolution (GE), which is one of the evolutionary algorithms. The generated program is applied for controlling EV3. The results show that the robot behavior in the experiment of EV3 was roughly equivalent to it in simulator. Therefore, the proposed algorithm based on GE can generate the control program of robot automatically.

DOI： 10.1109/ICRAE53653.2021.9657773

Scopus

隊列走行車両群の分流走行挙動シミュレーション Reviewed

北　栄輔，高上寛斗，玉城龍洋，村井希

日本計算数理工学論文集 Vol. 20 page： 95 - 100 2020.12

Simulation of Vehicles Platoons Passing Through Intersection Reviewed

Eisuke Kita, Shuji Hayashi, Tatsuhiro Tamaki

International Structural Engineering and Construction Emerging Technologies and Sustainability Principles 2020.11

Stock Price Prediction by Using Hybrid Sequential Generative Adversarial Networks Reviewed

Bate He, Eisuke Kita

2020 IEEE International Conference on Data Mining 2020.11

確率推定モデルを用いた株価予測手法について

北栄輔, 中村典雅, 城之内奈

計力スクエア 2020.11

交差点交通流の速度制御に関する研究

北栄輔, 風間亮, 村井希

計力スクエア 2020.11

非通信車両を含めた合流挙動に対応できる車両隊列走行の設計

北栄輔，大野颯汰

計力スクエア 2020.11

Stochastic Schemata Exploiter-Based Optimization of Convolutional Neural Network Reviewed

Hiroya Makino, Xuanang Feng, Eisuke Kita

2020 IEEE International Conference on Systems, Man, and Cybernetics 2020.10

Pedestrian Walking Direction Prediction Using Generative Adversarial Networks Reviewed

Bate He, Eisuke Kita

2020 IEEE International Conference on Systems, Man, and Cybernetics 2020.10

Vehicle Velocity Control in Case of Two Vehicle Platoon Merging into One Vehicle Platoon Invited Reviewed

Eisuke Kita, Miichiro Yamada

International Journal of Machine Learning and Computing Vol. 10 ( 5 ) page： - - 2020.9

ナスダック指数を用いたベイジアンネットワークによる株価予測モデル Invited Reviewed

中村典雅, 北田宙士, 北栄輔

第131回情報処理学会数理モデル化と問題解決研究会研究報告 2020.9

SNS 投稿画像の注目度向上のためのHuman-in-the-Loop を用いたタグ推薦手法の検討 Invited Reviewed

神野悦太郎，小野瀬良佑，北栄輔

第131回情報処理学会数理モデル化と問題解決研究会研究報告 2020.9

Vehicle Control in Platoon around Intersection Reviewed

E. Kita, S. Hayashi, T. Tamaki

5th International Conference on Engineering and Technology 2020.3

Genetic algorithm based optimization of deep convolutional neural network ensemble for pedestrian moving direction recognition, Reviewed

Xuanang Feng, Rui Li, Jianing Zhao, Eisuke Kita.

26th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific Neural Network Society, Sydney, Australia 2019.12

Optimal Schedule of Planting Day in Rice Cultivation Operation,

Etsutaro Kamino, Atsushi Uchiyama, Shunsaku Nishiuchi, Eisuke Kita

Asia-Pacific Conference on Computational Mechanics (APCOM2019), 2019.12

Velocity control of platoon vehicle in consideration of ride quality,

Eisuke Kita, Masashi Takahashi, Tatsuhiro Tamaki, Atsushi Uchiyama, Kana Idei.

Asia-Pacific Conference on Computational Mechanics (APCOM2019) 2019.12

多台参照追従モデルによる隊列走行シミュレーションについて Invited Reviewed

北　栄輔，朝比奈鋼司，牛田千智，玉城龍洋，村井希

日本計算数理工学論文集 Vol. 19 page： 79 - 84 2019.12

Overtaking Behavior Simulation of Vehicle Platoon in Face-to-Face Traffic Flow, Reviewed

Eisuke Kita, Hiroki Sakamoto, Tatuhiro Tamaki.

4th International Conference on Robotics and Automation Engineering (ICRAE 2019), 20191122 2019.11

Genetic algorithm based optimization of deep neural network ensemble for personal identification with face and voice features, Reviewed

Xuanang Feng, Jianing Zhao, Bate He, Eisuke Kita.

The 5th International Conference on Fuzzy Systems and Data Mining, Kitakyushu, Book series Frontiers in Artificial Intelligence and Applications (FAIA) 2019.10

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Papers - KITA, Eisuke