prasadovhal
Business Data Scientist at Red Hat. M.Tech in Mathematical Modeling & Simulation
Red HatPune
Pinned Repositories
A-Simple-Method-of-Solution-For-Multi-label-Feature-Selection
Multi-label learning has been a topic of research interest in multimedia, text & speech recognitions, music, image processing, information retrieval etc. In Multi-label classification (MLC) each instance is associated with a set of multiple class labels. Like other machine learning algorithms, data preprocessing plays an key role in MLC. Feature selection is an important preprocessing step in MLC, due to high dimensionality of datasets and associated computational costs. Extracting the most informative features considerably reduces the computational loads of MLC. Most of the Multi-label feature selection algorithms available in literature involve conversions to multiple single labeled feature selection problems. We proposed an efficient modification of a recent multi-label feature selection algorithm [1] available in literature. Our algorithm consists of two steps: in the first step we decompose the output label space into lower dimensions using simple matrix factorization method; subsequently we employ feature selection process in the decoupled reduced space. Our simulations with real world datasets reveal the efficiency of proposed framework.
beammech
Python module for studying deflection and stresses in loaded beams.
C-Codes
C Codes
datasciencecoursera
Dummy repo
datasharing
The Leek group guide to data sharing
Machine-Learning-Codes
All Machine Learning Codes completed during Masters in R and Python
Numerical-Computing-Codes
Numerical Computing codes : Differentiation ,Integration, Root finding ,Interpolation, Regression,
Scilab-Codes
Scilab Codes
Statistical-Inference-Codes
Statistical Inference Codes
Stochastic-Optimization-Codes
All Stochastic Optimization Codes during Masters
prasadovhal's Repositories
prasadovhal/A-Simple-Method-of-Solution-For-Multi-label-Feature-Selection
Multi-label learning has been a topic of research interest in multimedia, text & speech recognitions, music, image processing, information retrieval etc. In Multi-label classification (MLC) each instance is associated with a set of multiple class labels. Like other machine learning algorithms, data preprocessing plays an key role in MLC. Feature selection is an important preprocessing step in MLC, due to high dimensionality of datasets and associated computational costs. Extracting the most informative features considerably reduces the computational loads of MLC. Most of the Multi-label feature selection algorithms available in literature involve conversions to multiple single labeled feature selection problems. We proposed an efficient modification of a recent multi-label feature selection algorithm [1] available in literature. Our algorithm consists of two steps: in the first step we decompose the output label space into lower dimensions using simple matrix factorization method; subsequently we employ feature selection process in the decoupled reduced space. Our simulations with real world datasets reveal the efficiency of proposed framework.
prasadovhal/Scilab-Codes
Scilab Codes
prasadovhal/Statistical-Inference-Codes
Statistical Inference Codes
prasadovhal/Stochastic-Optimization-Codes
All Stochastic Optimization Codes during Masters
prasadovhal/beammech
Python module for studying deflection and stresses in loaded beams.
prasadovhal/C-Codes
C Codes
prasadovhal/datasciencecoursera
Dummy repo
prasadovhal/datasharing
The Leek group guide to data sharing
prasadovhal/Machine-Learning-Codes
All Machine Learning Codes completed during Masters in R and Python
prasadovhal/Numerical-Computing-Codes
Numerical Computing codes : Differentiation ,Integration, Root finding ,Interpolation, Regression,
prasadovhal/Deep-math-machine-learning.ai
A blog which talks about machine learning, deep learning algorithms and the Math. and Machine learning algorithms written from scratch.
prasadovhal/DO180-apps
DO180 Repository for Sample Applications
prasadovhal/it-cert-automation-practice
Google IT Automation with Python Professional Certificate - Practice files
prasadovhal/Netlogo-Codes
Netlogo Codes : Agent Based Modelling
prasadovhal/prasadovhal.github.io
prasadovhal/Python-Codes
Basic Python Codes
prasadovhal/R-Codes
R Codes
prasadovhal/scikit-feature
open-source feature selection repository in python (DMML Lab@ASU)
prasadovhal/shap
A unified approach to explain the output of any machine learning model.
prasadovhal/su2code.github.io
SU2 Project Website
prasadovhal/test-repo
Testing for github training