Advertisement

Ad code

Research Paper on Machine Learning

 

  Research Papers on Classifiers and Regression Models


In this article, I am going to write on two most important research papers which are related to comparison of list of classification and regression techniques. If you are interested in learning several supervised techniques then you must refer following two papers.

 

These research findings are very useful for machine learning fans. Paper 1st is on comparison of classification techniques and 2nd is about comparison of large collection of popular regression techniques. I also given URLs of these papers for download. Besides, you can easily access our 77 regression models.

 

  • Paper 1: Do we need hundreds of classifiers to solve real world classification problems?

This research paper focuses on 179 classifiers over 121 datasets from 17 machine learning families.


Figure 1 - Machine Learning Classification Families

 

Classification techniques are implemented in R, Weka, Matlab and C. According to the study, random forest classifier is the most likely to be the best classifier. Download this paper from link https://bit.ly/1yAuJa9 


  • Paper 2: An extensive experimental survey of regression methods.

Paper second is on machine learning regression techniques, published in neural network. It explains and compares 77 the most important models which belong to 19 machine learning families. Techniques are evaluated on 83 UCI regression datasets. Most of the techniques are implemented in R. I also mentioned list of Regression Techniques with their R package and references in my earlier article. Figure 2 shows 19 regression families.


Figure 2 - Machine Learning Regression Families


You can download above paper from link https://bit.ly/2J2OmTV 

Our code of 77 regression models is now available. Download it from https://bit.ly/2Y9LyI5 

And downloaded code you can try for your regression problem. 
  
Kindly follow my blog and stay tuned for more advanced post on dataset splitting. 

Thank you!


Post a Comment

0 Comments

Comments