Open Science Research Excellence
@article{(International Science Index):http://waset.org/publications/10008694,
  title    = {Hybrid Reliability-Similarity-Based Approach for Supervised Machine Learning},
  author    = {Walid Cherif},
  country   = {Morocco},
  institution={National Institute of Statistics and Applied Economics},
  abstract  = {Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.
},
    journal   = {International Journal of Computer, Electrical, Automation, Control and Information Engineering},  volume    = {12},
  number    = {3},
  year      = {2018},
  pages     = {170 - 175},
  ee        = {http://waset.org/publications/10008694},
  url       = {http://waset.org/Publications?p=135},
  bibsource = {http://waset.org/Publications},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 135, 2018},
}