We present a classification and regression algorithm called Random Bits Forest (RBF). RBF integrates neural network (for depth), boosting (for wideness) and random forest (for accuracy). It first generates and selects ~10,000 small three-layer threshold random neural networks as basis by gradient boosting scheme. These binary basis are then feed into a modified random forest algorithm to obtain predictions. In conclusion, RBF is a novel framework that performs strongly especially on data with large size.

Features

  • big data
  • Random Bits
  • neural network
  • boosting
  • random forest
  • machine learning
  • data mining
  • prediction

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Additional Project Details

Languages

English

Programming Language

C, C++

Related Categories

C++ Neural Network Libraries, C++ Big Data Tool, C Neural Network Libraries, C Big Data Tool

Registered

2015-10-10