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專利授權區
專利名稱(中)	用於隨機森林模型之優化方法及其系統
專利名稱(英)	RANDOM FOREST OPTIMIZATION METHOD AND SYSTEM THEREOF
專利家族	中華民國：202601465(公開號) 美國：US-2026-0004201-A1(公開號)
專利權人	國立清華大學 100.00%
發明人	葉維彰
技術領域	資訊工程,工業工程

專利摘要(中)
本揭示內容提供一種用於隨機森林模型之優化方法，包含：依據簡化群體生成規則產生設定值組。將設定值組轉換為隨機森林模型的複數決策樹代碼及對應決策樹代碼的複數權重值，其中此些決策樹代碼對應複數二元決策樹模型。計算設定值組對應的精確度及決策樹數量，其中精確度為隨機森林模型的一預測精確度，決策樹數量為此些二元決策樹模型的數量。依據設定值組的精確度及決策樹數量更新資料庫的最佳精確度及最低決策樹數量。重複執行上述步驟，直到設定值組的數量等於預設數量。藉此，減少隨機森林模型之決策樹數量，並有效縮減運算時間。

專利摘要(中)

本揭示內容提供一種用於隨機森林模型之優化方法，包含：依據簡化群體生成規則產生設定值組。將設定值組轉換為隨機森林模型的複數決策樹代碼及對應決策樹代碼的複數權重值，其中此些決策樹代碼對應複數二元決策樹模型。計算設定值組對應的精確度及決策樹數量，其中精確度為隨機森林模型的一預測精確度，決策樹數量為此些二元決策樹模型的數量。依據設定值組的精確度及決策樹數量更新資料庫的最佳精確度及最低決策樹數量。重複執行上述步驟，直到設定值組的數量等於預設數量。藉此，減少隨機森林模型之決策樹數量，並有效縮減運算時間。

專利摘要(英)
A random forest optimization method is proposed. The random forest optimization method includes generating a setting value group according to a Simplification Swarm Optimization (SSO) rule, and transforming the setting value group into a plurality of decision tree codes and a plurality of weight values corresponding to the decision tree codes of the random forest model. The decision tree codes correspond to a plurality of binary decision tree models. An accuracy and a decision tree number corresponding to the setting value group is calculated. The accuracy is a predicting accuracy of the random forest model, and the decision tree number is a number of the binary decision tree models. A best accuracy and a lowest decision tree number in a database are updated according to the accuracy and the decision tree number of the setting value group. The aforementioned steps are executed repeatedly until a number of the setting value group equal to a predetermine number. Thus, the random forest optimization method of the present disclosure can reduce a decision tree number of the random forest model and reduce the computing time effectively.

專利摘要(英)

A random forest optimization method is proposed. The random forest optimization method includes generating a setting value group according to a Simplification Swarm Optimization (SSO) rule, and transforming the setting value group into a plurality of decision tree codes and a plurality of weight values corresponding to the decision tree codes of the random forest model. The decision tree codes correspond to a plurality of binary decision tree models. An accuracy and a decision tree number corresponding to the setting value group is calculated. The accuracy is a predicting accuracy of the random forest model, and the decision tree number is a number of the binary decision tree models. A best accuracy and a lowest decision tree number in a database are updated according to the accuracy and the decision tree number of the setting value group. The aforementioned steps are executed repeatedly until a number of the setting value group equal to a predetermine number. Thus, the random forest optimization method of the present disclosure can reduce a decision tree number of the random forest model and reduce the computing time effectively.

聯絡資訊
承辦人姓名	黃允恬
承辦人電話	(03)571-5131#62305
承辦人Email	yuntian@mx.nthu.edu.tw