# Data Classification With Multiprocessing

arXiv: https://arxiv.org/abs/2312.15152 · HTML: https://mlsanity.com/paper/2312.15152/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 59 references that are in the map.
- First posted on arXiv 23 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [A parallel corpus of Python functions and documentation strings for automated code documentation and code generation](https://mlsanity.com/paper/1707.02275/) (2017)
- [Computer Vision Based Parking Optimization System](https://mlsanity.com/paper/2201.00095/) (2022)
- [MailLeak: Obfuscation-Robust Character Extraction Using Transfer Learning](https://mlsanity.com/paper/2012.11775/) (2020)
- [Indoor Air Quality Improvement](https://mlsanity.com/paper/2012.15387/) (2020)
- [Chatbot for fitness management using IBM Watson](https://mlsanity.com/paper/2112.15167/) (2021)
- [Machine Learning and Artificial Intelligence in Next-Generation Wireless Network](https://mlsanity.com/paper/2202.01690/) (2021)
- [ML-based Secure Low-Power Communication in Adversarial Contexts](https://mlsanity.com/paper/2212.13689/) (2022)
- [A Secured Protocol for IoT Networks](https://mlsanity.com/paper/2012.11072/) (2020)
- [Heterogeneous Computing Systems](https://mlsanity.com/paper/2212.14418/) (2022)
- [Investigations of Smart Health Reliability](https://mlsanity.com/paper/2112.15169/) (2021)
- [Efficient Semantic Segmentation on Edge Devices](https://mlsanity.com/paper/2212.13691/) (2022)

## Abstract (arXiv)

Classification is one of the most important tasks in Machine Learning (ML) and with recent advancements in artificial intelligence (AI) it is important to find efficient ways to implement it. Generally, the choice of classification algorithm depends on the data it is dealing with, and accuracy of the algorithm depends on the hyperparameters it is tuned with. One way is to check the accuracy of the algorithms by executing it with different hyperparameters serially and then selecting the parameters that give the highest accuracy to predict the final output. This paper proposes another way where the algorithm is parallelly trained with different hyperparameters to reduce the execution time. In the end, results from all the trained variations of the algorithms are ensembled to exploit the parallelism and improve the accuracy of prediction. Python multiprocessing is used to test this hypothesis with different classification algorithms such as K-Nearest Neighbors (KNN), Support Vector Machines (SVM), random forest and decision tree and reviews factors affecting parallelism. Ensembled output considers the predictions from all processes and final class is the one predicted by maximum number of processes. Doing this increases the reliability of predictions. We conclude that ensembling improves accuracy and multiprocessing reduces execution time for selected algorithms.