Combining Instance and Feature Neighbors for Efficient Multi-label Classification

Abstract

Multi-label classification problems occur naturally in different domains. For example, within text categorization the goal is to predict a set of topics for a document, and within image scene classification the goal is to assign labels to different objects in an image. In this work we propose a combination of two variations of k nearest neighborhoods (kNN) where the first neighborhood is computed instance (or row) based and the second neighborhood is feature (or column) based. Instance based kNN is inspired by user-based collaborative filtering, while feature kNN is inspired by item-based collaborative filtering. Finally we apply a linear combination of instance and feature neighbors scores and apply a single threshold to predict the set of labels. Experiments on various multi-label datasets show that our algorithm outperforms other state-of-the-art methods such as ML-kNN, IBLR and Binary Relevance with SVM, on different evaluation metrics. Finally our algorithm uses an inverted index during neighborhood search and scales to extreme datasets that have millions of instances, features and labels.