複合式的商業分析學解決方案於提升誤判群組存活率

A Hybrid Approach To Increase The Survivals Of False Positives

方昱傑(Yu-Jie Fang)

指導教授 ： 黃俊哲

國立暨南國際大學/管理學院/資訊管理學系/碩士(2017年)

摘要

如何去尋找核心目標並實施有效的決策，對於商業管理分析是最主要的問題，本研究提出一個嶄新的複合資料分析方法，使用邏輯回歸的總體預測能力分類出誤判他們存活的群組，這是在數據整體上最接近存活但死亡的群組；因此對誤判群組實施決策是最有效率的，再利用約略集合約減的特性，發現變項間隱藏的規則，再透過規則之間的近似比對，找出誤判群組為何無法存活的關鍵因素，最終提出交叉參考決策表提供決策者多元決策方式。本研究最後利用家庭節約能源案例，使用我們的方法如何在龐大的數據中產出交叉參考決策表，實施精確有效的決策。

關鍵字

方法 ； 誤判群組 ； 約略集合 ； 邏輯回歸 ； 決策程序

並列摘要

The main issue concerning Business Intelligence and Analytics is how to identify critical target audience and make an effective decision to achieve the goal. This study proposes a rough set theory incorporated with logistic regression, which can evaluate the key variables more accurately and induct the important rules in particular. By using the predictive ability of logistic regression from all dataset, false positives which are the closest to the survivals of dead groups will be able to be classified. Next, the rough set theory is used to generate the reducts and induct decision rules. Through the comparison between the rules belonged to the false positives and the rule base, the key issues about the dying situation of false positives are focused and resolved. Ultimately, the cross reference decision table provides multiple policy making modes to decision makers. The household energy saving case is studied to validate the superiority of the proposed solution approach.

並列關鍵字

Methodology ； False positives ； Rough sets ； Logistic regression ； Decision procedure

參考文獻

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