In the machine learning field, especially in classification tasks, model’s design and construction are very important. Constructing model via a limited set of features may sometimes bound performance lead to non-optimal performances that some algorithms can provide. To this end, Ensemble methods were proposed literature. These methods’ main goal is learn models provide or predictions whose join...

Swarm intelligence techniques with incredible success rates are broadly used for various irregular and interdisciplinary topics. However, their impact on ensemble models is considerably unexplored. This study proposes an optimized-ensemble model integrated smart home energy consumption management based learning particle swarm optimization (PSO). The proposed exploits PSO in two distinct ways; f...

We present a tile-based approach for producing clinically relevant probability maps of prostatic carcinoma in histological sections from radical prostatectomy. Our methodology incorporates ensemble learning for feature selection and classification on expert-annotated images. Random forest feature selection performed over varying training sets provides a subset of generalized CIEL*a*b* co-occurr...

Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. In this study we compare 32 feature selection methods on 4 public gene exp...