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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/72103


    Title: Performance Study on Tree-based Classification Algorithms for Smartphone Indoor/outdoor Detection
    Authors: 李佩臻;Lee,Pei-Chen
    Contributors: 資訊工程學系
    Keywords: 室內外定位;機器學習;手機感測器;indoor/outdoor detection;machine learning;IMU
    Date: 2016-07-27
    Issue Date: 2016-10-13 14:26:23 (UTC+8)
    Publisher: 國立中央大學
    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。;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.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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