majority-voting

[TIFS 2019] Skeleton-based Gait Recognition via Robust Frame-level Matching (RFM)

This project classifies diseases in grape plant using various Machine Learning classification algorithms.

GenPark AI Agent Skill - Self-consistency sampling aggregator, majority voting consensus engine, and semantic clusterer for reasoning verification.

GenPark AI Agent Skill - Self-consistency sampling aggregator, majority voting consensus engine, and semantic clusterer for reasoning verification.

Self-Consistency majority voting aggregator computing consensus answers, confidence scores, and Shannon entropy

Self-Consistency majority voting aggregator computing consensus answers, confidence scores, and Shannon entropy

Self-Consistency majority voting engine aggregating multiple stochastic reasoning paths to extract high-confidence consensus solutions.

A Credit Card Fraud Detection System using Adaboost and Majority Voting, designed to identify fraudulent credit card transactions by combining the strength of multiple classifiers.

Self-Consistency majority voting engine aggregating multiple stochastic reasoning paths to extract high-confidence consensus solutions.

Multi-agent ensemble voting with Borda count, Condorcet analysis, and confidence-weighted majority resolution

Multi-agent ensemble voting with Borda count, Condorcet analysis, and confidence-weighted majority resolution

I use an agent-based model to explore the impact of imperfect competence and social influence on majority voting. This repository contains the code for an agent-based model and simulations of majority voting, for producing some figures, and for a statistical analysis.

Open-source voting client + server with state-of-the-art systems like Graduated Majority Judgment and Approval Voting for fast, reactive collective decision-making.

Experimental results capturing the limits on annotation noise under which MV can aggregate labels optimally.

Application for soft voting algorithm demonstration

This repo contains bagging, variance of a model by drawing random bootstrap samples from the training dataset and combining the individually trained classifiers via majority vote; AdaBoost and gradient boosting, which are algorithms based on training weak learners that learn from mistakes.

An evaluation of prompting techniques (Zero-Shot CoT, Few-Shot, Self-Consistency) on the Mistral-7B model for mathematical reasoning. This project systematically benchmarks 7 distinct methods on the GSM8K dataset.

Multi-Agent Reinforcement Learning framework for collaborative label aggregation on noisy classification datasets. Includes DQN agents, Gym-style environment, evaluation tools, and majority-voting baseline.

Experimental implementation of Concept Bottleneck Models for explainable image classification under noisy annotations, on CUB-200-2011.

Worked on a classification analysis (class imbalance) for a business problem. Analysis was done using Anaconda Python.