| DC 欄位 |
值 |
語言 |
| DC.contributor | 資訊工程學系 | zh_TW |
| DC.creator | 李佩臻 | zh_TW |
| DC.creator | Pei-Chen Lee | en_US |
| dc.date.accessioned | 2016-7-27T07:39:07Z | |
| dc.date.available | 2016-7-27T07:39:07Z | |
| dc.date.issued | 2016 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=103522055 | |
| dc.contributor.department | 資訊工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 近年來，室內外定位偵測發展蓬勃，已經成為現今不可或缺的技術之一，並具有許多潛在的應用。舉例來說，可以透過定位使用者是否有從室外環境進入到室內環境， 以便協助使用者手機自動關閉GPS功能來節省能源，或是讓使用者手機自動切換靜音模式。本論文利用tree-based learning 演算法 (i.e., decision trees, boosting, bagging, and random forest) 來分類室內與室外定位的資料。並使用10-fold cross validation來驗證分類結果，以避免分類結果有overfitting的問題發生。最後比較各個tree-based learning 演算法分類結果的性能，並找出其中最適合分類室內外定位資料的tree-based learning演算法。在我們實驗中，雖然分類結果普遍偏高，但其中以boosting的演算法為最佳。Boosting在室內外定位資料分析有高達99%以上的正確率。 | zh_TW |
| dc.description.abstract | The indoor/outdoor detection for wireless device has many potential applications. For instance, when a device is detected to enter the indoor environment, it can turn off the GPS chip to save energy. In this thesis, the indoor/outdoor detection is treated as the supervised learning problem. The tree-based learning algorithms (i.e., decision trees, boosting, bagging, and random forest) are built from the training dataset and used to classify test dataset. In addition, the 10-fold cross over is used with the algorithms to mitigate the issue of overfitting. The performance of each algorithm are compared to identify the algorithm most appropriate for the indoor/outdoor detection. Although the final performance of each algorithm seems to be high, boosting provides the best accuracy (99%) for indoor/outdoor detection. | en_US |
| DC.subject | 室內外定位 | zh_TW |
| DC.subject | 機器學習 | zh_TW |
| DC.subject | 手機感測器 | zh_TW |
| DC.subject | indoor/outdoor detection | en_US |
| DC.subject | machine learning | en_US |
| DC.subject | IMU | en_US |
| DC.title | Performance Study on Tree-based Classification Algorithms for Smartphone Indoor/outdoor Detection | en_US |
| dc.language.iso | en_US | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |