A Novel Feature Extraction and Classification Technique for Machine Learning Using Time Series and Statistical Approach

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 33)

Abstract

Curse of dimensionality is a major challenge for any arena of scientific research like data mining, machine learning, optimization, clustering etc. An optimized feature extraction or dimension reduction gives rise to better prediction and classification, which can be applied to various research areas such as Bioinformatics, Geographical Information System, Speech Recognition, Image processing, Biometric, Biomedical imaging, letter or character recognition etc. Extracting the informative feature as well as removing the redundant and unwanted feature by reducing the data dimension is a remarkable issue for many scientific communities. In this paper we have introduced a novel feature extraction with dimension reduction technique by using combined signal processing and statistical approach as Discrete Wavelet Transform (DWT) and Multidimensional Scaling (MDS) respectively then Support Vector Machine (SVM) has played a major role for classification of nonlinear, heterogeneous dataset.

Keywords

Computer aided diagnosis Discrete wavelet transform (DWT) Multidimensional scaling (MDS) Support vector machines (SVM) Geographical information system (GIS) Principal component analysis (PCA) Factor analysis(FA) 

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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringVikash College of Engineering for WomenBargarhIndia
  2. 2.Department of Information TechnologyGovernment (SSD) Intermediate Science CollegeBalisankara, SundergarhIndia

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