不完全な命令決定システムにおけるクラスに基づく属性還元
Chunyuan Chen1,2, Feng Yin3,4, LiangKun Wu1
1School of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, 610041, China.
Scientific reports
|August 30, 2025
まとめ
この研究は,不完全な順序付けの決定システムにおける属性の削減のための新しい方法を導入し,効率と精度を向上させます. HANDRアルゴリズムは 不完全なデータで多基準の意思決定を強化します
科学分野:
- データサイエンス
- 人工知能
- 機械学習
背景:
- 多重基準の意思決定には,優先順位で並べられたデータを用いて,多重基準の概要集法 (DRSA) が広く使用されています.
- リアルなデータは不完全な情報によってしばしば課題を提示し,強固な属性削減技術を必要とします.
- 不完全な順序決定システム (IODS) の伝統的な属性評価方法は,計算的に高価であり,分類の影響を無視する可能性があります.
研究 の 目的:
- 不完全な命令決定システム (IODS) のための新しい属性削減アプローチを提案する.
- 既存の方法の限界に対処する属性の重要性評価のための新しい基準を開発する.
- 属性削減アルゴリズムの効率と分類精度を高める.
主な方法:
- 定義されたクラス間近さと,IODS内の全体のクラス間非支配的概念.
- 拡張された支配マトリックスを用いて,クラス間の非支配性を計算する方法を開発した.
- クラス間非優位性 (HANDR) アルゴリズムに基づくヒューリスティック属性減少を設計し,ヒューリスティック検索戦略を組み込みました.
主要な成果:
- 提案されたHANDRアルゴリズムは,従来の方法と比較して実行時間の有意な改善を示しています.
- 実験では,UCIデータセットのHANDRアルゴリズムを使用して分類精度が顕著に増加しています.
- クラス間の全体的な非優位性は,IODSにおける属性の重要性の有効な基準であることが証明されています.
結論:
- HANDRアルゴリズムは,不完全な命令決定システムにおける属性の削減のための効率的で正確なソリューションを提供します.
- 提案された評価基準は,全体的な階級間の非優位性であり,意思決定における不完全な情報の課題を効果的に解決します.
- この研究は,複雑で現実的なデータセットのための多基準の意思決定技術の進歩に寄与します.
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