Ordinal Decision Trees

Ordinal Decision Trees are a machine learning approach specifically designed to handle ordinal classification tasks, where the labels exhibit a natural order. Unlike nominal classification, which treats all classes as equally distinct, ordinal classification takes the ordinal relationship into account, producing more accurate and relevant results. This is particularly critical in applications where the magnitude of classification errors has implications. Despite their importance, ordinal classification problems are often tackled using nominal methods, leading to suboptimal solutions. This work conducts an experimental study of tree-based methodologies specifically designed to capture ordinal relationships. A comprehensive survey of ordinal splitting criteria is provided, standardizing the notations used in the literature for clarity. Three ordinal splitting criteria, Ordinal Gini (OGini), Weighted Information Gain (WIG), and Ranking Impurity (RI), are compared to the nominal counterparts of the first two (Gini and information gain), by incorporating them into a decision tree classifier. An extensive repository considering 45 publicly available OC datasets is presented, supporting the first experimental comparison of ordinal and nominal splitting criteria using well-known OC evaluation metrics. Statistical analysis of the results highlights OGini as the most effective ordinal splitting criterion to date.

Category: Artificial Intelligence
Subcategory: Machine Learning
Tags: Ordinal Decision Treesordinal classificationsplitting criteriaOrdinal GiniWeighted Information GainRanking Impurity
AI Type: Machine Learning
Programming Languages: Not specified
Frameworks/Libraries: Not specified
Application Areas: Ordinal classification tasks
Manufacturer Company: Not specified
Country: Not specified
Algorithms Used

Decision Trees

Model Architecture

Ordinal Splitting Criteria

Datasets Used

45 publicly available OC datasets

Performance Metrics

Ordinal Gini, Weighted Information Gain, Ranking Impurity

Deployment Options

Not specified

Cloud Based

No

On Premises

No

Features

Captures ordinal relationships, provides more accurate results

Enterprise

No

Hardware Requirements

Not specified

Supported Platforms

Not specified

Interoperability

Not specified

Security Features

Not specified

Compliance Standards

Not specified

Certifications

Not specified

Open Source

No

Source Code URL

http://Not specified

Documentation URL

http://Not specified

Community Support

Not specified

Contributors

Not specified

Training Data Size

Not specified

Inference Latency

Not specified

Energy Efficiency

Not specified

Explainability Features

Not specified

Ethical Considerations

Not specified

Known Limitations

Not specified

Industry Verticals

Not specified

Use Cases

Improved ordinal classification

Customer Base

Not specified

Integration Options

Not specified

Scalability

Not specified

Support Options

Not specified

SLA

Not specified

User Interface

Not specified

Multi-Language Support

No

Localization

Not specified

Pricing Model

Not specified

Trial Availability

No

Partner Ecosystem

Not specified

Patent Information

Not specified

Regulatory Compliance

Not specified

Version

Not specified

Website URL

http://Not specified

Service Type

Not specified

Has API

No

API Details

Not specified

Business Model

Not specified

Price

0.00

Currency

Not specified

License Type

Not specified

Release Date

01/01/1970

Last Update Date

01/01/1970

Contact Email

Not specified

Contact Phone

Not specified

Social Media Links

http://Not specified

Other Features

Not specified

Published

Yes