Assessing innovation capabilities of manufacturing companies by combination of unsupervised and supervised machine learning approaches

This study suggests the use of unsupervised and supervised machine learning algorithms to categorize companies according to their innovation capabilities. Companies are categorized into three groups: good, satisfactory, and unsatisfactory, in order to create a thorough and reliable assessment procedure. In this study, unsupervised and supervised machine learning methods are used to solve an innovation capability evaluation problem. Data is provided via a survey which is performed in manufacturing industry in Turkiye Firstly, dimensions of innovation capability were determined Principal Component Analysis (PCA). Then data labels were determined by k-means clustering algorithm which is an unsupervised learning technique. A model is first trained using data provided via questionnaire survey, and it is then tested using fresh, unused data. The model is trained using classification algorithms including KNN, GaussianNB, RandomForest, Gradient Boosting, AdaBoost, DesisionTree, XGBOOST and LightGBMC, MLPC, and SVMC and its performance is evaluated against test data. Each classification techniques are evaluated using the performance metrics. With the highest accuracy rate of 93% and lowest MAE, MSE and RMSE values, The LightGBMC and SVMC methods were found the most efficient supervised learning method for innovation capability evaluation.& COPY; 2023 Elsevier B.V. All rights reserved.

Automated summary

This study proposes the use of unsupervised and supervised machine learning algorithms to categorize companies based on their innovation capabilities. The companies are divided into three groups: good, satisfactory, and unsatisfactory, in order to establish a comprehensive and reliable assessment procedure. The study utilizes unsupervised and supervised machine learning methods to address the innovation capability evaluation problem.