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.
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