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Accelerating Extreme Classification via Adaptive Feature Agglomeration
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README.md

DEFRAG

Introduction

Accelerating Extreme Classification via Adaptive Feature Agglomeration

Running DEFRAG

DEFARG is executed in two steps:

  • defrag_clustering: This computes a grouping of features.
  • defrag_agglomeration: This agglomerates the features based on groupings obtained from previous step.

Please refer to sample_run.py for more information on how to use DEFRAG.

Feature and label files should be formatted as expected by Parabel.

Parameters

Following parameters can be tuned in DEFRAG

defrag_clustering

fr = param.feature_representation : Use feture repersentation X or XY, default 1 (X).
cml = param.cluster_maxleaf : Maximum number of features in a leaf node of DEFRAG tree, default 8.
cls = param.cluster_label_sample : Percentage of labels used for clustering, default 5.
cds = param.cluster_data_sample : Percentage of data points used for clustering, default 20.

defrag_agglomeraton

avg = param.avg : Average out non-zero entries while agglomeration, default 0"<<endl;

Acknowledgement

The code is adapted and subsequently modified from the source code provided by the authors of Parabel: Partitioned Label Trees for Extreme Classification with Application to Dynamic Search Advertising.

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