Mineichi Kudo

Also known as 工藤峰一

Mineichi Kudo is a pattern recognition researcher at Hokkaido University whose work covers feature selection, large-scale classification, multi-label learning and rare-event recognition. At Hokkaido University, he was an assistant in 1990 and 1991, an associate professor in 1995, and a professor from 2001. A 1998 paper with Jack Sklansky compared feature selectors for pattern classifiers, and in December 2001 the two received the Twenty-Seventh Annual Pattern Recognition Society Award. The International Association for Pattern Recognition named him a Fellow in 2008 for contributions to pattern classification and feature extraction, and the Institute of Electronics, Information and Communication Engineers conferred its Fellow title in 2019. He is a professor in the Mathematical and Computational Sciences field of Hokkaido University's Faculty of Information Science and Technology, and he has led Grant-in-Aid projects on multi-label classification and rare events.

Now at: Hokkaido University
Works: 245
Citations: 2,862
h-index: 24
Badges: Loyal

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

1990–2001

Assistant to professor at Hokkaido

In 1990 and 1991 he is an assistant in Hokkaido University's Faculty of Engineering, and in 1995 he is an associate professor there. A professorship in its Graduate School of Engineering begins in 2001. In December 2001, he and Jack Sklansky receive the Twenty-Seventh Annual Pattern Recognition Society Award.
1998

Comparing feature selectors

In 1998, he and Jack Sklansky publish "A comparative evaluation of medium- and large-scale feature selectors for pattern classifiers" in Kybernetika. The paper proposes a unified way to compare many feature selection algorithms and finds sequential floating algorithms promising up to medium problems and genetic algorithms for medium and large ones.
2002–2005

Large-scale classification project

As principal investigator of the Grant-in-Aid project "Development of Classification Systems for Large-Scale Pattern Recognition Problems and Its Application," he works on scaling in data number, dimensionality and class number. The project invents a fast k-nearest neighbor method, studies feature selection for problems with many features, and groups hard-to-classify classes into "super classes" for problems with many categories such as Kanji-character recognition.
2008

An IAPR Fellowship

In 2008, the International Association for Pattern Recognition names him a Fellow, with a citation for contributions to pattern classification and feature extraction.
2015–2017

Multi-label classification project

He is principal investigator of the Grant-in-Aid project eXtFS on feature selection and exploration for extremely large multi-label classification problems, in which an object receives several labels. Its results point to the importance of correlations between labels and to dividing the samples by their features or labels to keep processing time realistic. One of its papers, written with Lu Sun for Pattern Recognition in 2018, is "Optimization of classifier chains via conditional likelihood maximization."
2019

An IEICE Fellow

The Institute of Electronics, Information and Communication Engineers confers its Fellow title on him in March 2019.
2019–2024

Rare-event recognition project

From April 2019 to March 2024, he leads the Grant-in-Aid project MARE, on recognition and discovery techniques for giving awareness. The group builds techniques for discovering and classifying rare events such as rare diseases and anomalies of elderly people living alone, including a behavior simulator in a virtual smart home.
As of October 2026

Professor at Hokkaido University

He is a professor in the Mathematical and Computational Sciences field of Hokkaido University's Faculty of Information Science and Technology. In May 2025, he coauthors "Multi-view multi-label personalized classification via generalized exclusive sparse tensor factorization" in Knowledge and Information Systems.