Book ChapterDOI
An Axiomatic Fuzzy Set Theory Based Feature Selection Methodology for Handwritten Numeral Recognition
Abhinaba Roy,Nibaran Das,Ram Sarkar,Subhadip Basu,Mahantapas Kundu,Mita Nasipuri +5 more
- pp 133-140
TLDR
From the experimental results, it has been found that the methodology provides higher recognition accuracies with lesser or equal numbers of features selected for each dataset.Abstract:
A new feature selection methodology on the basis of features’ combined class separability power, using the framework of Axiomatic Fuzzy Set (AFS) theory has been proposed here The AFS theory provides the rules for logic operations needed to interpret the combinations of features from the fuzzy feature set Based on these combinational rules, class separability power of the combined features is determined and subsequently the most powerful subset of the feature set is selected The performance of this methodology is evaluated upon for recognition of handwritten numerals of five popular Indic scripts viz Bangla, Devanagari, Roman, Telugu and Arabic with SVM based classifier using gradient based directional feature set and quad-tree based longest-run feature set separately and compared with six widely used feature selection techniques From the experimental results, it has been found that the methodology provides higher recognition accuracies with lesser or equal numbers of features selected for each datasetread more
Citations
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Journal ArticleDOI
A multi-objective approach towards cost effective isolated handwritten Bangla character and digit recognition
TL;DR: A multi-objective region sampling methodology for isolated handwritten Bangla characters and digits recognition has been proposed and an AFS theory based fuzzy logic is utilized to develop a model for combining the pareto-optimal solutions from two multi- objective heuristics algorithms.
Journal ArticleDOI
A multi-scale deep quad tree based feature extraction method for the recognition of isolated handwritten characters of popular indic scripts
TL;DR: In the present work, a non-explicit feature based approach, more specifically, a multi-column multi-scale convolutional neural network (MMCNN) based architecture has been proposed for this purpose and a deep quad-tree based staggered prediction model has be proposed for faster character recognition.
Journal ArticleDOI
A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition
TL;DR: An overview of some of the methods and approach of feature extraction and selection in handwriting character recognition, and the review of metaheuristic harmony search algorithm (HSA) has provide.
Journal ArticleDOI
Devanagari Handwritten Character Recognition using fine-tuned Deep Convolutional Neural Network on trivial dataset
TL;DR: A two-stage approach of deep learning is developed to enhance overall success of the proposed Devanagari Handwritten Character Recognition System (DHCRS), which requires very fewer trainable parameters and notably less training time to achieve state-of-the-art performance on a very small dataset.
Journal ArticleDOI
Fuzzy time series forecasting based on axiomatic fuzzy set theory
TL;DR: This study takes the distribution of data into account to position time series in the framework of fuzzy sets and develops a method to determine the prototypes based on the corresponding fuzzy description of the clusters, which can effectively improve forecasting accuracy.
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Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
TL;DR: In this article, the maximal statistical dependency criterion based on mutual information (mRMR) was proposed to select good features according to the maximal dependency condition. But the problem of feature selection is not solved by directly implementing mRMR.
Feature selection based on mutual information: criteria ofmax-dependency, max-relevance, and min-redundancy
TL;DR: This work derives an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection, and presents a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers).
Correlation-based Feature Selection for Machine Learning
TL;DR: This thesis addresses the problem of feature selection for machine learning through a correlation based approach with CFS (Correlation based Feature Selection), an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy.