On the public citation graph

Novel Classification Algorithm for Complex Class Structures

0 signed · 19 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s11042-022-14195-yopenalex-extracted
Guiding genetic search algorithm with ANN based fitness function: a case study using structured HOG descriptors for license plate detection
inferredcites →https://doi.org/10.1007/s44230-023-00041-3openalex-extracted
Applications and Techniques of Machine Learning in Cancer Classification: A Systematic Review
inferredcites →https://doi.org/10.1016/j.asoc.2023.110020openalex-extracted
Weighted kappa measures for ordinal multi-class classification performance
inferredcites →https://doi.org/10.1016/j.asoc.2023.110415openalex-extracted
A broad review on class imbalance learning techniques
inferredcites →https://doi.org/10.1016/j.asoc.2023.110662openalex-extracted
A competitive learning scheme for deep neural network pattern classifier training
inferredcites →https://doi.org/10.1016/j.eswa.2020.113856openalex-extracted
Fuzzy C-Means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: Review and development
inferredcites →https://doi.org/10.1016/j.eswa.2023.120377openalex-extracted
A fuzzy C-means algorithm for optimizing data clustering
inferredcites →https://doi.org/10.1016/j.neucom.2019.12.090openalex-extracted
A comparative study of general fuzzy min-max neural networks for pattern classification problems
inferredcites →https://doi.org/10.1016/j.procs.2021.12.258openalex-extracted
An Efficient Signature Recognition System Based on Gradient Features and Neural Network Classifier
inferredcites →https://doi.org/10.1109/72.846747openalex-extracted
General fuzzy min-max neural network for clustering and classification
inferredcites →https://doi.org/10.1109/cvprw59228.2023.00332openalex-extracted
K-means Clustering Based Feature Consistency Alignment for Label-free Model Evaluation
inferredcites →https://doi.org/10.1109/tfuzz.2019.2924396openalex-extracted
An Improved Fuzzy Min–Max Neural Network for Data Classification
inferredcites →https://doi.org/10.14569/ijacsa.2021.0120670openalex-extracted
Classification Model Evaluation Metrics
inferredcites →https://doi.org/10.3390/a13070158openalex-extracted
Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients
inferredcites →https://doi.org/10.3390/app11125573openalex-extracted
SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot
inferredcites →https://doi.org/10.3390/e25071021openalex-extracted
Fuzzy C-Means Clustering: A Review of Applications in Breast Cancer Detection
inferredcites →https://doi.org/10.3390/math11051081openalex-extracted
A Review on Nature-Inspired Algorithms for Cancer Disease Prediction and Classification
inferredcites →https://doi.org/10.5220/0009163405060512openalex-extracted
Classification of Histopathological Images using Scale-Invariant Feature Transform
inferredcites →https://doi.org/10.9734/jsrr/2023/v29i121812openalex-extracted
Partition Coefficient and Partition Entropy in Fuzzy C Means Clustering
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.