Study of Engineered Features and Learning Features in Machine Learning – A Case Study in Document Classification

Study of Engineered Features and Learning Features in Machine Learning – A Case Study in Document Classification

Abstract:. Document classification is challenging due to handling of voluminous and highly non-linear data, generated exponentially in the era of digitization. Proper representation of documents increases efficiency and performance of classification, ultimate goal of retrieving information from large corpus. Deep neural network models learn features for document classification unlike the engineered feature based approaches where features are extracted or selected from the data. In the paper we investigate performance of different classifiers based on the features obtained using two approaches. We apply deep autoencoder for learning features while engineering features are extracted by exploiting semantic association within the terms of the documents. Experimentally it has been observed that learning feature based classification always perform better than the proposed engineering feature based classifiers.

Conclusion and Future Work: In the paper we emphasize the importance of feature representation for classification. The potential of deep learning in feature extraction process for efficient compression and representation of raw features is explored. By conducting multiple experiments we deduce that a DBN – Deep AE feature extractor and a DNNC outperforms most other techniques providing a trade-off between accuracy and execution time. In this paper we have dealt with the most significant feature extraction and classification techniques for text documents where each text document belongs to a single class label. With the explosion of digital information a large number of documents may belong to multiple class labels handling of which is a new challenge and scope of future work. Word2vec models [18] in association with Recurrent Neural Networks(RNN) [4,14] have recently started gaining popularity in feature representation domain. We would like to compare their performance with our deep learning method in future. Similar feature extraction techniques can also be applied to image data to generate compressed feature which can facilitate efficient classification. We would also like to explore such possibilities in our future work.

Study of Engineered Features and Learning Features in Machine Learning – A Case Study in Document Classification

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