7%

5%

the accuracy of gender prediction has increased

by

by

the accuracy of age prediction has increased

Technical solution

1. Bringing expert system assessments to a common format for ensemble

2. Developed an algorithm for combining the assessment of expert systems for predicting gender and age using video images from CCTV cameras

3. We evaluated the performance of the ensemble system using test data.

Ensembling is a machine learning method that combines multiple models (expert estimates) to improve forecast quality. Each individual model has strengths and weaknesses, and ensemble allows for increased stability of the developed model and improved forecast accuracy.

4. Optimized the use of a limited amount of labeled data. Machine learning algorithms and active learning techniques were applied to improve forecasting accuracy and efficiency.

5. Implemented the developed ensemble system

6. The following approaches were used:

weighted voting

an ensemble method where each model in the ensemble is weighted according to its importance in decision making

algebraic methods

allow you to process data, identify patterns, reduce data dimensionality, build models, and make decisions based on algebraic calculations

neural networks

class of machine learning algorithms

approximation by hyperplanes

Business values

Increased accuracy

Gender prediction accuracy increased by 7%

Age prediction accuracy has increased by 5%
The developed systems estimate gender and age based on multiple expert assessments, which allows for more reliable results.

Optimizing the use of limited data

One of the key features of the project is the ability to use only final estimates obtained from expert systems, which reduces the likelihood of errors and increases the accuracy of estimates.

Development of software
and Big Data solutions

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