Explication of a logistic regression driven hypothesis to strengthen derivative approach driven classification for medical diagnosis

Saurabh Kumar Agarwal, Rajesh Kumar

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)

Abstract

Supervised learning based classification depends on learning from previously known data set. Here, these data sets governs training for classification of new data points. This training is mainly driven by two fundamental approaches. First one is derivative based approach and another centers around heuristics or direct search based methodologies. Both approaches have their pros and cons depending upon realization and formulation of problem. Heuristic based approaches gains an edge over derivative based approaches while solving a non-deterministic problem model in terms of accuracy. Derivative based approaches are faster whose reliability is only limited to deterministic problem model. This paper explicate a logistic regression driven hypothesis to convert non-deterministic neural network model into a deterministic model to support derivative based approaches. Experiments were performed and extensive comparison has been done with other algorithms are tabulated. High accuracy of result supports the use this new method.

Original languageEnglish
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event2014 IEEE Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2014 - Bhopal, India
Duration: 1 Mar 20142 Mar 2014

Conference

Conference2014 IEEE Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2014
Country/TerritoryIndia
CityBhopal
Period1/03/142/03/14

Keywords

  • Artificial Neural Network (ANN)
  • Classification
  • Derivative based approach
  • Hypothesis
  • Medical diagnosis

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Electrical and Electronic Engineering

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