Generalized Zero-Shot Learning (GZSL) aims to recognize classes in the test dataset that do not have image samples in the training dataset. GZSL tasks typically place all classes into a semantic space using predefined semantic attributes for seen and unseen classes. Since there are no real images for unseen classes, classifying these classes correctly is a challenging task. To overcome this challenge, synthetic features for unseen classes are generated using generative networks. Based on this approach, we proposed a generator-based GZSL model that selects the best samples using machine learning methods for the generated synthetic features. In our proposed model, we preferred semantically rich representations instead of traditional semantic attributes for semantic information representation. Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) were used together to generate synthetic features. We applied k-means and DBSCAN clustering algorithms to the generated synthetic features and then classified them. To evaluate our proposed model, we conducted experiments on well-known GZSL benchmark datasets AWA2, CUB, and FLO. We extended our experiments to include open-set classes. Comprehensive experiments showed GZSL classification performances of 67.8% on AWA2, 77.0% on CUB, and 92.4% on FLO. Additionally, we observed the improving effect of k-means and DBSCAN clustering algorithms on GZSL classification performance.