A supervised learning task involves constructing a mapping from an input data space (normally described by several features) to an output space. A set of training examples---examples with known output values---is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. Within supervised learning, one type of task is a classification learning task, in which each output consists of one or more classes to which the corresponding input belongs. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate's measurements. In this chapter, we explain several basic classification algorithms.

This is the classification based E-commerce text dataset for 4 categories - "Electronics", "Household", "Books" and "Clothing & Accessories", which almost cover 80% of any E-commerce website.

The dataset is in ".csv" format with two columns - the first column is the class name and the second one is the datapoint of that class. The data point is the product and description from the e-commerce website.

The dataset has the following features :

Data Set Characteristics: Multivariate

Number of Instances: 50425

Number of classes: 4

Area: Computer science

Attribute Characteristics: Real

Number of Attributes: 1

Associated Tasks: Classification

Missing Values? No

Gautam. (2019). E commerce text dataset (version - 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3355823

Content

This dataset contains a cleaned version of this dataset https://www.kaggle.com/rikdifos/credit-card-approval-prediction on credit cards.

A supervised learning task involves constructing a mapping from an input data space (normally described by several features) to an output space. A set of training examples---examples with known output values---is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. Within supervised learning, one type of task is a classification learning task, in which each output consists of one or more classes to which the corresponding input belongs. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate's measurements. In this chapter, we explain several basic classification algorithms.

The Data Classification Market Report is Segmented by Component (Software and Services), Classification Method (Content-Based, Context-Based, and More), Organization Size (Large Enterprises and Small and Medium Enterprises (SMEs)), Application (Access Control and IAM, Governance and Compliance, and More), Industry Vertical (BFSI, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Data Classification Market Outlook

According to our latest research, the global Data Classification market size reached USD 1.92 billion in 2024, with a robust year-over-year growth rate. The market is projected to expand at a CAGR of 23.4% from 2025 to 2033, positioning it to reach a forecasted value of USD 13.34 billion by 2033. The primary growth driver for this market is the accelerating adoption of advanced data security solutions across industries, as organizations seek to comply with stringent data privacy regulations and mitigate the risks associated with data breaches.

The increasing frequency and sophistication of cyber threats have made data classification a critical component of enterprise security strategies. Organizations are prioritizing the deployment of data classification solutions to identify, categorize, and protect sensitive information, ensuring that only authorized personnel have access to critical data assets. This shift is further fueled by the proliferation of cloud computing and digital transformation initiatives, which have led to exponential growth in data volumes and complexity. As a result, the demand for automated and scalable data classification tools is surging, enabling businesses to maintain visibility and control over their data in real time.

Another significant growth factor is the evolving regulatory landscape, with governments and industry bodies around the world introducing rigorous data protection laws such as GDPR, CCPA, and HIPAA. Compliance with these regulations necessitates robust data classification frameworks to accurately assess and report on the handling of personally identifiable information (PII) and other sensitive data types. Enterprises are increasingly investing in data classification solutions to avoid severe penalties, enhance audit readiness, and demonstrate accountability in their data management practices. This trend is particularly pronounced in highly regulated sectors such as BFSI, healthcare, and government, where the stakes for data protection are exceptionally high.

The integration of artificial intelligence and machine learning into data classification platforms is also propelling market growth. These technologies enable more accurate and efficient classification by automating the identification of sensitive data patterns, reducing manual intervention, and minimizing the risk of human error. AI-driven solutions can adapt to evolving data environments and emerging threats, offering predictive analytics and real-time insights that empower organizations to make informed security decisions. This technological advancement is expected to further accelerate the adoption of data classification tools across diverse industry verticals.

Regionally, North America remains the dominant market for data classification, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The United States, in particular, exhibits strong demand due to the presence of major technology companies, a mature cybersecurity ecosystem, and stringent regulatory requirements. Meanwhile, the Asia Pacific region is experiencing the fastest growth, driven by rapid digitalization, increasing cybercrime incidents, and growing awareness of data privacy issues among enterprises. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a comparatively nascent stage, as organizations in these regions ramp up their investments in data security infrastructure.

Component Analysis

The Data Classification market is segmented by component into Software and Services, each playing a pivotal role in the overall ecosystem. Software solutions dominate the market, accounting for a substantial portion of the total revenue. These solutions are designed to automate the identification, labeling, and categorization of data based on predefined policies and rules. The evolution of software offerings has been marked by the integration of advanced analytics, machine learning, and artificial intelligence

Problem Statement: We’re excited to launch a unique challenge in the lead-up to MLDS 2025, where your skills in fine-tuning Small language models (SLMs) will be tested. This hackathon focuses on multi-class classification—your task is to fine-tune an SLM to classify data into multiple categories using the provided dataset accurately.

1) Data introduction • Bill_authentication dataset is a dataset for decision tree classification.

2) Data utilization (1)Bill_authentication data has characteristics that: • Data aimed at decision-making classification through five variables including change, curtosis, and entropy. (2)Bill_authentication data can be used to: • Fraud detection: Financial institutions and businesses can use this data to develop models to detect counterfeit bills, enhance security, and reduce financial losses due to fraud. • Feature analysis: Researchers can analyze datasets to identify the features that best represent counterfeit bills and provide insight into the characteristics of genuine and counterfeit bills.

This is the official data repository of the Data-Centric Image Classification (DCIC) Benchmark. The goal of this benchmark is to measure the impact of tuning the dataset instead of the model for a variety of image classification datasets. Full details about the collection process, the structure and automatic download at

Paper: https://arxiv.org/abs/2207.06214

Source Code: https://github.com/Emprime/dcic

The license information is given below as download.

Citation

Please cite as

@article{schmarje2022benchmark,
  author = {Schmarje, Lars and Grossmann, Vasco and Zelenka, Claudius and Dippel, Sabine and Kiko, Rainer and Oszust, Mariusz and Pastell, Matti and Stracke, Jenny and Valros, Anna and Volkmann, Nina and Koch, Reinahrd},
  journal = {36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks},
  title = {{Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation}},
  year = {2022}
}

Please see the full details about the used datasets below, which should also be cited as part of the license.

@article{schoening2020Megafauna,
author = {Schoening, T and Purser, A and Langenk{\"{a}}mper, D and Suck, I and Taylor, J and Cuvelier, D and Lins, L and Simon-Lled{\'{o}}, E and Marcon, Y and Jones, D O B and Nattkemper, T and K{\"{o}}ser, K and Zurowietz, M and Greinert, J and Gomes-Pereira, J},
doi = {10.5194/bg-17-3115-2020},
journal = {Biogeosciences},
number = {12},
pages = {3115--3133},
title = {{Megafauna community assessment of polymetallic-nodule fields with cameras: platform and methodology comparison}},
volume = {17},
year = {2020}
}

@article{Langenkamper2020GearStudy,
author = {Langenk{\"{a}}mper, Daniel and van Kevelaer, Robin and Purser, Autun and Nattkemper, Tim W},
doi = {10.3389/fmars.2020.00506},
issn = {2296-7745},
journal = {Frontiers in Marine Science},
title = {{Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification}},
volume = {7},
year = {2020}
}


@article{peterson2019cifar10h,
author = {Peterson, Joshua and Battleday, Ruairidh and Griffiths, Thomas and Russakovsky, Olga},
doi = {10.1109/ICCV.2019.00971},
issn = {15505499},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
pages = {9616--9625},
title = {{Human uncertainty makes classification more robust}},
volume = {2019-Octob},
year = {2019}
}

@article{schmarje2019,
author = {Schmarje, Lars and Zelenka, Claudius and Geisen, Ulf and Gl{\"{u}}er, Claus-C. and Koch, Reinhard},
doi = {10.1007/978-3-030-33676-9_26},
issn = {23318422},
journal = {DAGM German Conference of Pattern Regocnition},
number = {November},
pages = {374--386},
publisher = {Springer},
title = {{2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy}},
volume = {11824 LNCS},
year = {2019}
}

@article{schmarje2021foc,
author = {Schmarje, Lars and Br{\"{u}}nger, Johannes and Santarossa, Monty and Schr{\"{o}}der, Simon-Martin and Kiko, Rainer and Koch, Reinhard},
doi = {10.3390/s21196661},
issn = {1424-8220},
journal = {Sensors},
number = {19},
pages = {6661},
title = {{Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy}},
volume = {21},
year = {2021}
}

@article{schmarje2022dc3,
author = {Schmarje, Lars and Santarossa, Monty and Schr{\"{o}}der, Simon-Martin and Zelenka, Claudius and Kiko, Rainer and Stracke, Jenny and Volkmann, Nina and Koch, Reinhard},
journal = {Proceedings of the European Conference on Computer Vision (ECCV)},
title = {{A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering}},
year = {2022}
}


@article{obuchowicz2020qualityMRI,
author = {Obuchowicz, Rafal and Oszust, Mariusz and Piorkowski, Adam},
doi = {10.1186/s12880-020-00505-z},
issn = {1471-2342},
journal = {BMC Medical Imaging},
number = {1},
pages = {109},
title = {{Interobserver variability in quality assessment of magnetic resonance images}},
volume = {20},
year = {2020}
}


@article{stepien2021cnnQuality,
author = {St{\c{e}}pie{\'{n}}, Igor and Obuchowicz, Rafa{\l} and Pi{\'{o}}rkowski, Adam and Oszust, Mariusz},
doi = {10.3390/s21041043},
issn = {1424-8220},
journal = {Sensors},
number = {4},
title = {{Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment}},
volume = {21},
year = {2021}
}

@article{volkmann2021turkeys,
author = {Volkmann, Nina and Br{\"{u}}nger, Johannes and Stracke, Jenny and Zelenka, Claudius and Koch, Reinhard and Kemper, Nicole and Spindler, Birgit},
doi = {10.3390/ani11092655},
journal = {Animals 2021},
pages = {1--13},
title = {{Learn to train: Improving training data for a neural network to detect pecking injuries in turkeys}},
volume = {11},
year = {2021}
}

@article{volkmann2022keypoint,
author = {Volkmann, Nina and Zelenka, Claudius and Devaraju, Archana Malavalli and Br{\"{u}}nger, Johannes and Stracke, Jenny and Spindler, Birgit and Kemper, Nicole and Koch, Reinhard},
doi = {10.3390/s22145188},
issn = {1424-8220},
journal = {Sensors},
number = {14},
pages = {5188},
title = {{Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks}},
volume = {22},
year = {2022}
}

Data Classification Market size was valued at USD 1664.66 Million in 2024 and is projected to reach USD 9486.25 Million by 2032, growing at a CAGR of 24.3% during the forecast period 2026-2032.

Global Data Classification Market Drivers

The market drivers for the Data Classification Market can be influenced by various factors. These may include:

Increasing Data Volume: In order to maintain data security, compliance, and effective use, there is an increasing requirement to manage and classify the data produced by enterprises in an exponentially growing amount. Regulatory Compliance: Organizations must categorize their data based on the sensitivity levels required by strict data protection laws like the GDPR, CCPA, HIPAA, and others. Adoption of data classification solutions is driven by compliance requirements, which guarantee adherence to regulatory standards and prevent heavy penalties.

Data Security Concerns: Organizations are concentrating on strengthening their data security procedures due to the increase in cyber threats and data breaches. Classifying data makes it easier to find sensitive information and implement the right security measures to keep it safe from theft or unwanted access.

Growing Adoption of Cloud Services: As cloud computing services become more widely used, strong data classification techniques are required to guarantee data security and compliance, particularly when data is transferred between different cloud environments and storage locations. Increasing Awareness of Data Privacy: The need for solutions that allow for better management and protection of sensitive data through classification and encryption is being driven by heightened awareness of data privacy issues among consumers and enterprises. Combining Data Loss Prevention (DLP) Systems: Through the identification, monitoring, and prevention of sensitive information leakage or unlawful transfer, data categorization integrated with DLP systems improves data protection capabilities. Emergence of AI and Machine Learning Technologies: By incorporating these technologies into data categorization systems, data may be identified and classified more automatically and accurately, saving labor and increasing efficiency. Demand for Data Governance and Lifecycle Management: In order to maintain data quality, integrity, and compliance throughout its lifecycle, organizations are realizing more and more how important it is to have effective data governance and lifecycle management. A key component of putting into practice efficient data governance procedures is data classification.

Discover the booming Data Classification Software market! Explore key trends, drivers, and restraints shaping this $5B (2025) industry, projected to reach $15B by 2033 with a 15% CAGR. Learn about leading vendors and regional market shares.

These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data.

With the scripts provided, you can extract CSV files from the UCI machine learning repository and from OpenML. The ordinal class labels stem from a binning of a continuous regression label.

We complement this data set with the indices of data items that appear in each sample of our evaluation. Hence, you can precisely replicate our samples by drawing the specified data items. The indices stem from two evaluation protocols that are well suited for ordinal quantification. To this end, each row in the files app_val_indices.csv, app_tst_indices.csv, app-oq_val_indices.csv, and app-oq_tst_indices.csv represents one sample.

Our first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification tasks, where classes are ordered and a similarity of neighboring classes can be assumed.

Usage

You can extract four CSV files through the provided script extract-oq.jl, which is conveniently wrapped in a Makefile. The Project.toml and Manifest.toml specify the Julia package dependencies, similar to a requirements file in Python.

Preliminaries: You have to have a working Julia installation. We have used Julia v1.6.5 in our experiments.

Data Extraction: In your terminal, you can call either

make

(recommended), or

julia --project="." --eval "using Pkg; Pkg.instantiate()"
julia --project="." extract-oq.jl

Outcome: The first row in each CSV file is the header. The first column, named "class_label", is the ordinal class.

Further Reading

Implementation of our experiments: https://github.com/mirkobunse/regularized-oq

The size of the Data Classification market was valued at USD 748.7 million in 2023 and is projected to reach USD 1750.51 million by 2032, with an expected CAGR of 12.9% during the forecast period.

Dataset for project: food-classification

  Dataset Description

This dataset has been processed for project food-classification.

  Languages

The BCP-47 code for the dataset's language is unk.

  Dataset Structure





  Data Instances

A sample from this dataset looks as follows: [ { "image": "<308x512 RGB PIL image>", "target": 0 }, { "image": "<512x512 RGB PIL image>", "target": 0 }]

  Dataset Fields

The dataset has the… See the full description on the dataset page: https://huggingface.co/datasets/Kaludi/data-food-classification.

1) Data Introduction • The Dummy Marketing Data for Classification dataset is a dummy dataset created by individuals for 'Data Science for Business' and 'Data-driven marketing' classes. It contains data on age, expenditure, region, and whether apps are downloaded.

2) Data Utilization (1) Dummy Marketing Data for Classification data has characteristics that: • The dataset includes 2 numerical variables, 2 category variables. (2) Dummy Marketing Data for Classification data can be used to: • Data Science classes: useful for training basic concepts and skills in data science, including data preprocessing, exploratory data analysis (EDA), feature engineering, model learning, and evaluation. • Marketing Analysis: Available as hands-on material in classes that teach marketing strategies and data-driven decision-making.

MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING MOHAMMAD SALIM AHMED, LATIFUR KHAN, NIKUNJ OZA, AND MANDAVA RAJESWARI Abstract. There has been a lot of research targeting text classification. Many of them focus on a particular characteristic of text data - multi-labelity. This arises due to the fact that a document may be associated with multiple classes at the same time. The consequence of such a characteristic is the low performance of traditional binary or multi-class classification techniques on multi-label text data. In this paper, we propose a text classification technique that considers this characteristic and provides very good performance. Our multi-label text classification approach is an extension of our previously formulated [3] multi-class text classification approach called SISC (Semi-supervised Impurity based Subspace Clustering). We call this new classification model as SISC-ML(SISC Multi-Label). Empirical evaluation on real world multi-label NASA ASRS (Aviation Safety Reporting System) data set reveals that our approach outperforms state-of-theart text classification as well as subspace clustering algorithms.

The Children vs Adults Classification dataset contains diverse, high-quality image data of individuals from various regions, categorized into children and adults. It is designed for machine learning applications such as facial recognition, healthcare, and demographic analytics.

1) Data Introduction • The Mushroom dataset focuses on identifying whether mushrooms are edible or poisonous based on their physical characteristics. This dataset, sourced from the UCI Machine Learning Repository, consists of 8124 instances detailing attributes like cap color, gill size, stalk shape, and odor, which are crucial for classification.

2) Data Utilization (1) Mushrooms data has characteristics that: • It includes comprehensive attributes from gilled mushrooms, categorized into various classes based on edibility. • The dataset is essential for developing machine learning models that can accurately classify mushrooms, which is critical for both educational purposes and practical applications. (2) Mushrooms data can be used to: • Educational Resource: Used in academic settings to teach students about biological data classification and the importance of accurate classification in environmental biology. • Food Safety: Assists in the development of automated systems to help foragers and consumers distinguish between safe and toxic mushrooms.

Sensitive Document Classification

Preventing data violation becomes increasingly crucial. Several data breaches have been reported during the last years. To prevent data violation, we need to determine the sensitivity level of documents. Deep learning techniques perform well in document classification but require large amount of data. However, a lack of public dataset in this context, due to the sensitive nature of documents, prevent reseacher to to design powerful models. We… See the full description on the dataset page: https://huggingface.co/datasets/mouhamet/sensitive_document_classification.

With the advent and expansion of social networking, the amount of generated text data has seen a sharp increase. In order to handle such a huge volume of text data, new and improved text mining techniques are a necessity. One of the characteristics of text data that makes text mining difficult, is multi-labelity. In order to build a robust and effective text classification method which is an integral part of text mining research, we must consider this property more closely. This kind of property is not unique to text data as it can be found in non-text (e.g., numeric) data as well. However, in text data, it is most prevalent. This property also puts the text classification problem in the domain of multi-label classification (MLC), where each instance is associated with a subset of class-labels instead of a single class, as in conventional classification. In this paper, we explore how the generation of pseudo labels (i.e., combinations of existing class labels) can help us in performing better text classification and under what kind of circumstances. During the classification, the high and sparse dimensionality of text data has also been considered. Although, here we are proposing and evaluating a text classification technique, our main focus is on the handling of the multi-labelity of text data while utilizing the correlation among multiple labels existing in the data set. Our text classification technique is called pseudo-LSC (pseudo-Label Based Subspace Clustering). It is a subspace clustering algorithm that considers the high and sparse dimensionality as well as the correlation among different class labels during the classification process to provide better performance than existing approaches. Results on three real world multi-label data sets provide us insight into how the multi-labelity is handled in our classification process and shows the effectiveness of our approach.