eXtFS:Feature Selection and Exploration in extremly large multi-label classification problems

2017 Fiscal Year Final Research Report

• Project/Area Number: 15H02719
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Single-year Grants
• Section: 一般
• Research Field: Perceptual information processing
• Research Institution: Hokkaido University
• Principal Investigator: Kudo Mineichi 北海道大学, 情報科学研究科, 教授 (60205101)
• Co-Investigator(Kenkyū-buntansha): 今井 英幸 北海道大学, 情報科学研究科, 教授 (10213216) ; 中村 篤祥 北海道大学, 情報科学研究科, 准教授 (50344487)
• Project Period (FY): 2015-04-01 – 2018-03-31
• Keywords: マルチラベル識別 / スケーラビリティ / 確率構造 / 同時可視化 / 埋め込み

Outline of Final Research Achievements

We have dealt with "multi-label classification" problems where an object is assigned multiple labels. This study aimed at raising the classification performance and speeding up without degradation of performance. Our achievement is three of the following. First, we have pointed out the importance of the correlation between labels and showed several ways using it. Second, to keep a realistic processing time, we showed that the problem division of samples on the basis of their features or labels in some experimental results. Last, we pointed out the necessity of a special treatment on labels that appear rarely or have been forgotten to assign.

Free Research Field

パターン認識