AEAO: Auto Encoder with Adam Optimizer Method for Efficient Document Indexing of Big Data

Abstract

In the big data era, the document classification became an active research area due to the explosive nature in the volumes of data. Document Indexing is one of the important tasks under text classification. The objective of this research is to increase the performance of the document indexing by proposing Adam optimizer in the auto-encoder. Due to the larger dimension and multi-class classification problem, the accuracy of document indexing is reduced. In this paper, an enhanced auto encoder is used based on the objective function of the Adam optimization (AEAO), which improves the learning rate and accuracy of indexing. The documents from the 20-newsgroup data set are converted into vector representation, and then the cosine similarity and Pearson correlation have been measured from the vector. The word to vector representation has words in the vector form and the frequency of words in the document increases their value. The Adam optimization technique selects the features by using similarity values and improves the learning rate. The auto encoder classifier classifies the document based on the objective function of the Adam optimizer. The experiment is conducted using python and the result infers that the classification performance of AEAO is better than that of Similarity-based classification framework for Multiple-Instance Learning and Self-Adaptive LSH encoding for multi-instance Learning techniques in terms of parameters like precision, recall and f-score.