Wavelet Tree ensembles with Machine Learning and its classification

Abstract

Wavelet trees are compact data structures in computational geometry. In the past, it was used as an essential tool for handling the size of data, data compression, indexing and for many more applications. Machine learning algorithms are used for classification of data and its analysis. In this article, we discuss the scope of machine learning with wavelet trees, wavelet entropy, wavelet matrix and wavelet packets. The study concludes that machine learning applications with wavelet tree is a better choice in terms storage and classification of data. The proposed methodology consists of three techniques for making the data more efficient. It consists of LZW Compression techniques, Wavelet tree, and machine learning algorithm SVM. In this methodology compression with classification process is done for datasets. This proposed methodology performs with machine learningalgorithms in terms of classification of data. In future this method can be used for efficient searching and indexing of large data sets. The classified and compressed dataset perform the indexing with wavelet tree takes less searching time.