Multi-objective optimisation is a process used to find solutions that balance two or more goals at the same time. Instead of looking for a single best answer, it tries to find a set of options that represent the best possible trade-offs between competing objectives. This approach is important when improving one goal makes another goal…
Category: Data Science
Knowledge Integration Networks
Knowledge Integration Networks are systems that connect information, expertise and insights from different sources to create a more complete and useful understanding. They help people or organisations bring together knowledge that might be scattered across departments, databases or even different organisations. By linking and organising this information, these networks make it easier to solve complex…
Temporal Knowledge Modeling
Temporal knowledge modelling is a way of organising information that changes over time. It helps computers and people understand not just facts, but also when those facts are true or relevant. This approach allows systems to keep track of events, sequences, and the duration of different states or relationships. For example, a person’s job history…
Anomaly Detection Optimization
Anomaly detection optimisation involves improving the methods used to find unusual patterns or outliers in data. This process focuses on making detection systems more accurate and efficient, so they can spot problems or rare events quickly and with fewer errors. Techniques might include fine-tuning algorithms, selecting better features, or adjusting thresholds to reduce false alarms…
Active Inference Pipelines
Active inference pipelines are systems that use a process of prediction and correction to guide decision-making. They work by continuously gathering information from their environment, making predictions about what will happen next, and then updating their understanding based on what actually happens. This helps the system become better at achieving goals, as it learns from…
Feature Correlation Analysis
Feature correlation analysis is a technique used to measure how strongly two or more variables relate to each other within a dataset. This helps to identify which features move together, which can be helpful when building predictive models. By understanding these relationships, one can avoid including redundant information or spot patterns that might be important…
Cross-Task Knowledge Transfer
Cross-Task Knowledge Transfer is when skills or knowledge learned from one task are used to improve performance on a different but related task. This approach is often used in machine learning, where a model trained on one type of data or problem can help solve another. It saves time and resources because the system does…
Symbolic Reasoning Integration
Symbolic reasoning integration is the process of combining traditional logic-based reasoning methods with modern data-driven approaches like machine learning. This integration allows systems to use explicit rules and symbols, such as if-then statements or mathematical logic, alongside statistical learning. The goal is to create smarter systems that can both learn from data and apply clear,…
Domain-Agnostic Learning
Domain-agnostic learning is a machine learning approach where models are designed to work across different fields or types of data without being specifically trained for one area. This means the system can handle information from various sources, like text, images, or numbers, and still perform well. The goal is to create flexible tools that do…
Knowledge-Driven Analytics
Knowledge-driven analytics is an approach to analysing data that uses existing knowledge, such as expert opinions, rules, or prior experience, to guide and interpret the analysis. This method combines data analysis with human understanding to produce more meaningful insights. It helps organisations make better decisions by considering not just raw data, but also what is…