Improved rule discovery performance on uncertainty

Tolun M., Sever H.

RESEARCH AND DEVELOPMENT IN KNOWLEDGE DISCOVERY AND DATA MINING, cilt.1394, ss.310-321, 1998 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 1394
  • Basım Tarihi: 1998
  • Dergi Adı: RESEARCH AND DEVELOPMENT IN KNOWLEDGE DISCOVERY AND DATA MINING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, EMBASE, MathSciNet, Philosopher's Index, zbMATH
  • Sayfa Sayıları: ss.310-321
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

In this paper we describe the improved version of a novel rule induction algorithm, namely ILA. We first outline the basic algorithm, and then present how the algorithm is enhanced using the new evaluation metric that handles uncertainty in a given data set. In addition to having a faster induction than the original one, we believe that our contribution comes into picture with a new metric that allows users to define their preferences through a penalty factor. We use this penalty factor to tackle with over-fitting bias, which is inherently found in a great many of inductive algorithms. We compare the improved algorithm ILA-2 to a variety of induction algorithms, including ID3, OC1, C4.5, CN2, and ILA. According to our preliminary experimental work, the algorithm appears to be comparable to the well-known algorithms such as CN2 and C4.5 in terms of accuracy and size.