Investigating The gcForest Algorithm

William Vorhies describes a new algorithm with strong potential:

gcForest (multi-Grained Cascade Forest) is a decision tree ensemble approach in which the cascade structure of deep nets is retained but where the opaque edges and node neurons are replaced by groups of random forests paired with completely-random tree forests.  In this case, typically two of each for a total of four in each cascade layer.

Image and text problems are categorized as ‘feature learning’ or ‘representation learning’ problems where features are neither predefined nor engineered as in traditional ML problems.  And the basic rule in these feature discovery problems is to go deep, using multiple layers each of which learns relevant features of the data in order to classify them.  Hence the multi-layer structure so familiar with DNNs is retained.

By using both random forests and completely-random tree forests the authors gain the advantage of diversity.  Each forest contains 500 completely random trees allowed to split until each leaf node contains only the same class of instances making the growth self-limiting and adaptive, unlike the fixed and large depth required by DNNs.

The estimated class distribution forms a class vector which is then concatenated with the original feature vector to be the input of the next level cascade.  Not dissimilar from CNNs.

The final model is a cascade of cascade forests.  The final prediction is obtained by aggregating the class vectors and selecting the class with the highest maximum score.

Click through for how it fares on the normal sample data sets like MNIST.

Related Posts

Methods To Improve Model Accuracy

Tristan Robinson shows how to go back to the drawing board when your model’s accuracy isn’t cutting it: One of the reoccurring principles that appears with machine learning is that of Ockham’s razor, which states that the best models are simple models that fit the data well; this is not an irrefutable principle of logic, but […]

Read More

JupyterLab Now Available

Project Jupyter announces the general availability of JupyterLab: JupyterLab is an interactive development environment for working with notebooks, code and data. Most importantly, JupyterLab has full support for Jupyter notebooks. Additionally, JupyterLab enables you to use text editors, terminals, data file viewers, and other custom components side by side with notebooks in a tabbed work area. JupyterLab […]

Read More

Leave a Reply

Your email address will not be published. Required fields are marked *

Categories

February 2018
MTWTFSS
« Jan  
 1234
567891011
12131415161718
19202122232425
262728