This actually questions the interpretability and stability of traditional feature selection algorithms. The high correlation in features frequently produces multiple equally optimal signatures, which makes traditional feature selection method unstable and thus leading to instability which reduces the confidence ...
The qualitative nature of the stability property enhardens the development of practical, stability optimizing, data mining algorithms as several issues naturally arise, such as: how ldquomuchrdquo stability is enough, or how can stability be effectively associated with intrinsic data properties. In the...
It uses computers as a platform, applies mathematical algorithms, and builds intelligent perception models. It has the ability to learn from experience, particularly from mistakes, and can be developed and perfected by learning from a large number of cases9,10,11.Given the abundance of classic ...
Incorporating network of prior knowledge into gene selection methods in general did not significantly improve classification accuracy, but greatly interpretability of gene signatures compared to classical algorithms.doi:10.1186/1471-2105-13-69Yupeng Cun...
we used ensemble machine learning algorithms, like bagging and boosting, to look at slope stability and predict FOS. Thereafter we reduce the dimension or features using the KPCA method and apply the same classification framework for analyzing slope stability. In the second part of our analysis, ...
The intuitiveness of this requirement has resulted in several works that study the stability of feature selection algorithms (Kalousis et al. 2007; Han and Yu 2010; Saeys et al. 2008; Loscalzo et al. 2009; Yu et al. 2008). Our work is substantially different from these approaches, since...
Next, we introduce two high-throughput algorithms designed to address the specific data processing needs for lateral and general loads. Lastly, details of the first-principles calculations embedded into these high-throughput algorithms are elucidated. 2.1. Tetragonal transformation of Al lattice It is ...
In this paper, we attempt to evaluate the consistency of explanations produced for process predictions by two popular explainable methods. We propose that methods and metrics to assess feature selection algorithms can be used to evaluate explanation stability. We use these metrics to assess ...
Furthermore, the data generated using numerical analysis was used to predict the factor of safety using hybridized ensembled methods considering the XGB model and three other novel meta-heuristic algorithms (MHA): political optimization (POA), the Leopard Seal algorithm (LSA), and the Giant ...
The design of mechanically stable MOFs has been a research hotspot. Moghadam et al. [158] were the first to develop the application of machine learning algorithms to predict the mechanical stability of MOFs. In order to locate MOFs with high mechanical stability, shear modulus is a useful ...