A Comparative Study of FCA-Based Supervised Classification Algorithms
A Comparative Study of FCA-Based Supervised Classification Algorithms
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DOI：
10.1007/978-3-540-24651-0_26
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发表时间：
2004-02
期刊：
影响因子：
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通讯作者：
Huaiyu Fu;Huaiguo Fu;Patrick Njiwoua;E. Nguifo
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文献类型：
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作者：
Huaiyu Fu;Huaiguo Fu;Patrick Njiwoua;E. Nguifo
目前已经提出了几种基于FCA的分类算法，如GRAND、Legal、Galois、RULEARNER、CIBLE和CLNN&CLNB。这些分类器已经与标准分类算法进行了比较，例如C4.5、朴素贝叶斯或IB1。除了在Legal和Cible之间进行比较外，它们从未在同一个平台上被相互比较过。在这里，我们从理论和实验上对它们进行了比较，并与标准机器学习算法C4.5进行了比较。并对实验结果进行了讨论。
Several FCA-based classification algorithms have been proposed, such as GRAND, LEGAL, GALOIS, RULEARNER, CIBLe, and CLNN & CLNB. These classifiers have been compared to standard classification algorithms such as C4.5, Naïve Bayes or IB1. They have never been compared each other in the same platform, except between LEGAL and CIBLe. Here we compare them together both theoretically and experimentally, and also with the standard machine learning algorithm C4.5. Experimental results are discussed.