曖昧なモデル-曖昧なクラスタリングアプローチに基づく新しいキャリア予測方法
1School of Education Science, Nanjing Normal University, Nanjing 210097, China; Jiangsu Vocational Institute of Commerce, Nanjing 211168, China.
Acta psychologica
|August 28, 2025
まとめ
この研究は キャリア予測のための新しい模糊モデルを導入し 個人の意思決定と 企業の採用を促進します このモデルは既存の方法よりも高い精度で キャリア選択や採用結果を改善しています
科学分野:
- 心理学について
- コンピュータ科学
- キャリア開発
背景:
- 効果的なキャリア決定は 職場の競争力と最適の採用に不可欠です
- キャリア予測ツールは 職業を理解し リスクを評価し 自信を高めるのに役立ちます
- キャリア予測の既存の方法は 不確実性と複雑なデータで 課題に直面しています
研究 の 目的:
- キャリアを予測するための模糊モデルを開発し評価する.
- キャリアの意思決定において 不確実性を処理する 曖昧な論理の能力を活用する
- 適した職業を特定する際の精度と効率を向上させる.
主な方法:
- 職業上の関心と人格の特徴 (RIASECコード) を含む模糊なモデルを適用した.
- 多くの入力変数を管理し,フージールール決定を簡素化するためにフージークラスタリングを使用しました.
- 予測精度をプロファイルと機械学習方法と比較した.
主要な成果:
- 曖昧なモデルは,従来のプロファイルと機械学習のアプローチよりも高い予測精度を示しました.
- 曖昧なクラスタリングは"曖昧な規則の爆発"の問題に効果的に対処しました.
- このモデルは,個々の職業の興味や人格の特徴を,適切な職業にマッピングすることに成功しました.
結論:
- 提案された模糊モデルは キャリア予測に より正確で科学的なアプローチを提供します
- キャリアの選択に伴う不確実性に対処するための 堅固な枠組みを提供します
- この方法は,個人が情報に基づいたキャリアの選択をしたり,雇用者が採用する際に大きく役立ちます.
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