假說選擇之於整體式學習之研究

Abstract

機器學習研究的目標之一是提高分類的準確性。過去幾十年，許多研究著重於根據問題領域和統計學習理論開發新的演算法，以不斷提高分類效能。最近很多研究人員發現，只有使用單一的分類算法時才會出現校能瓶頸，因為每種演算法雖然都有其優點，但也有其弱點。整體式學習，結合了幾個分類器或假設成為一個強大的分類器或學習器，依賴於各種假設的組合，而不是使用最新的算法。在整體式學習中，假設選擇對於效能非常重要，選擇假設的多樣性是一個重要的選擇標準。本篇論文提出了三種算法，其重點在於產生階層性假設結構以實現假設選擇的目標，其中基於特定標準來合併兩個假說。我們對8個數據集進行實驗，實驗結果證明，該方法優於隨機森林演算法。

One of the goals for the machine learning research is to improve the accuracy of the classification. Many research studies have focused on developing novel algorithms according to problem domains and statistical learning theory to continuously improve classification performance over the past decades. Recently, many researchers have found that performance bottleneck often occurs when only using a single classification algorithm, since each algorithm has its strength, but it also has its weakness. Ensemble learning, which combines several classifiers or hypotheses to become a strong classifier or learner, relies on the combination of various hypotheses rather than using state-of-the-art algorithms. In ensemble learning, hypothesis selection is crucial to performance, and the diversity of the selected hypotheses is an important selection criterion. This work proposes three algorithms focusing on generating a hierarchical hypothesis structure to achieve the goal of hypothesis selection, in which the two hypotheses are combined based on particular criterion. We conduct experiments on 8 data sets, and the experimental results indicate that the proposed method outperforms random forest, which is a state-of-the-art method.