Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Author: search.filters.author.Pavan, R.

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Performance evaluation of dimensionality reduction techniques on high dimensional data
    (Institute of Electrical and Electronics Engineers Inc., 2019) Vikram, M.; Pavan, R.; Dineshbhai, N.D.; Mohan, B.R.
    With a large amount of data being generated each day, the task on analyzing and making inferences from data is becoming an increasingly challenging task. One of the major challenges is the curse of dimensionality which is dealt with by using several popular dimensionality reduction techniques such as ICA, PCA, NMF etc. In this work, we make a systematic performance evaluation of the efficiency and effectiveness of various dimensionality reduction techniques. We present a rigorous evaluation of various techniques benchmarked on real-world datasets. This work is intended to assist data science practitioners to select the most suitable dimensionality reduction technique based on the trade-off between the corresponding effectiveness and efficiency. ©2019 IEEE.
  • No Thumbnail Available
    Item
    Bayesian optimization and gradient boosting to detect phishing websites
    (Institute of Electrical and Electronics Engineers Inc., 2021) Pavan, R.; Nara, M.; Gopinath, S.; Patil, N.
    We propose an Extreme Gradient Boosting framework for classification and regression problems emerging in machine learning for small-sized data sources sampled from a discrete distribution, i.e. data containing discrete or quantized attributes. The model parameters are iteratively refined from a prior belief for specific use cases using Bayesian optimization. We focus the application area of this framework on detecting fraudulent websites. With properly stated reasoning, we empirically test our methodology on a publicly available and bench-marked UCI Phishing dataset to demonstrate the superior performance of this approach as compared to existing methods in the literature. © 2021 IEEE.