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Effective Text Comment Classification Using Novel ML Algorithm - Modified Lazy Random Forest

EasyChair Preprint no. 3987

5 pagesDate: August 2, 2020


Machine learning (ML) algorithms are methods used to classify data. The various patterns or classes can be classified with the help of these various ML algorithms. There are numerous areas where these algorithms can be used. One such area is to detect whether the comment, sms or text message is SPAM or Normal message. So the aim of this work is to identify the best machine learning algorithms to detect SPAM text message on two different dataset. The first dataset is collected from YouTube comment dataset and second is the SMS dataset. The Random Forest (RF) is the ensemble learning method for classification, regression and other tasks that operates by developing a multitude of decision trees at learning phase and outputting the class. Its one variant which performs well as compare to normal RF is Lazy RF, as the study shown in [base paper ref], is the base for this research work. In this work, we have proposed one more novel variant of LRF and the different machine learning algorithms are compared in terms of accuracy with proposed Modified Lazy Random Forest. The results are compare with two techniques, first is the simple hold out, and second is the K-fold cross validation. For both cases the proposed algorithm performs well to detect the SPAM messages for the both datasets.

Keyphrases: deep learning, IDS, machine learning, Performance Matrices

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Tejashri Ghodke and Vijay Khadse},
  title = {Effective Text Comment Classification Using Novel ML Algorithm - Modified Lazy Random Forest},
  howpublished = {EasyChair Preprint no. 3987},

  year = {EasyChair, 2020}}
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