We investigate feature selection problem for generic machine learning models. We introduce a novel framework that selects features considering the outcomes of the model. Our framework introduces a novel feature maskin...
详细信息
We investigate feature selection problem for generic machine learning models. We introduce a novel framework that selects features considering the outcomes of the model. Our framework introduces a novel feature masking approach to eliminate the features during the selection process, instead of completely removing them from the dataset. This allows us to use the same machine learning model during feature selection, unlike other feature selection methods where we need to train the machine learning model again as the dataset has different dimensions on each iteration. We obtain the mask operator using the predictions of the machine learning model, which offers a comprehensive view on the subsets of the features essential for the predictive performance of the model. A variety of approaches exist in the feature selection literature. However, to our knowledge, no study has introduced a training-free framework for a generic machine learning model to select features while considering the importance of the feature subsets as a whole, instead of focusing on the individual features. We demonstrate significant performance improvements on the real-life datasets under different settings using LightGBM and multilayer perceptron as our machine learning models. Our results show that our methods outperform traditional feature selection techniques. Specifically, in experiments with the residential building dataset, our general binary mask optimization algorithm has reduced the mean squared error by up to 49% compared to conventional methods, achieving a mean squared error of 0.0044. The high performance of our general binary mask optimization algorithm stems from its feature masking approach to select features and its flexibility in the number of selected features. The algorithm selects features based on the validation performance of the machine learning model. Hence, the number of selected features is not predetermined and adjusts dynamically to the dataset. Additionally, we openly s
Sequential Function Chart (SFC) is one of the PLC programming languages defined in the IEC-61131 standard. SFC has been increasingly used in practical applications because even small controllers can now be programmed ...
详细信息
In recent times, AI and UAV have progressed significantly in several applications. This article analyzes applications of UAV with modern green computing in various sectors. It addresses cutting-edge technologies such ...
详细信息
Disease detection in agricultural crops plays a pivotal role in ensuring food security and sustainable farming practices. Deep learning models, known for their ability in image analysis, often demand extensive image d...
详细信息
The pursuit of enhanced interactive visual experiences has created growing interest in 360-degree video streaming. However, transmitting such content requires significant bandwidth compared to conventional planar vide...
详细信息
The maximum power transfer capability (MPTC) of phase-locked loop (PLL)-based grid-following inverters is often limited under weak-grid conditions due to passivity violations caused by operating-point-dependent contro...
详细信息
A high gain, circularly polarized array antenna is proposed with low profile and compact size using T-shaped top loading, t-matching, and a reflector. Composed of 4 individual elements, the array has a −3-dB impedance...
详细信息
Supply chain management and Hyperledger are two interconnected domains. They leverage blockchain technology to enhance efficiency, transparency, and security in supply chain operations. Together, they provide a decent...
详细信息
The article wants to draw attention to the potential occurrence of Braess-like phenomena in the context of cascade failures, where certain networked system configurations, which might appear more resilient than others...
详细信息
暂无评论