Variable selection from a feature representing protein sequences: a case of classification on bacterial type IV secreted effectors

Liu

Computational Intelligence and Neuroscience

2021

Self Cite

Machine learning plays an important role in computational intelligence and has been widely used in many engineering fields. Surface voids or bugholes frequently appearing on concrete surface after the casting process make the corresponding manual inspection time consuming, costly, labor intensive, and inconsistent. In order to make a better inspection of the concrete surface, automatic classification of concrete bugholes is needed. In this paper, a variable selection strategy is proposed for pursuing feature interpretability, together with an automatic ensemble classification designed for getting a better accuracy of the bughole classification. A texture feature deriving from the Gabor filter and gray-level run lengths is extracted in concrete surface images. Interpretable variables, which are also the components of the feature, are selected according to a presented cumulative voting strategy. An ensemble classifier with its base classifier automatically assigned is provided to detect whether a surface void exists in an image or not. Experimental results on 1000 image samples indicate the effectiveness of our method with a comparable prediction accuracy and model explicable.

Section: Methodsmentioning

confidence: 99%

Variable Selection from Image Texture Feature for Automatic Classification of Concrete Surface Voids

Liu

Computational Intelligence and Neuroscience

2021

Self Cite

VAWKUM trans. comput. sci.

“…Such approaches have been shown to significantly improve classification accuracy. The most frequently used methods for the ensemble are to improve and cover classification, are used for classification and regression [71].…”

Section: Decision Tree Classifier (Dtc)mentioning

confidence: 99%

AI and Machine Learning-based practices in various domains: A Survey

Ghulam,

Sikander,

Ali

2022

In several projects in computational biology (CB), bioinformatics, health informatics(HI), precision medicine(PM) and precision agriculture(PA) machine learning(ML) has become a primary resource. In this paper we studied the use of machine learning in the development of computational methods for top ﬁve research aeras. The last few years have seen an increased interest in Artiﬁcial Intelligence (AI), comprehensive ML and DL techniques for computational method development. Over the years, an enormous amount of research has been biomedical scientists still don’t have more knowledge to handle a biomedical projects eﬃciently and may, therefore, adopt wrong methods, which can lead to frequent errors or inﬂated tests. Healthcare has become a fruitful ground for artiﬁcial intelligence (AI) and machine learning due to the increase in the volume, diversity, and complexity of data (ML). Healthcare providers and life sciences businesses already use a variety of AI technologies. The review summarizes a traditional machine learning cycle, several machine learning algorithms, various techniques to data analysis, and effective use in ﬁve research areas. In this comprehensive review analysis, we proposed 10 ten rapid and accurate practices to use ML techniques in health informatics, bioinformatics, computational and systems biology, precision medicine and precision agriculture, avoid some common mistakes that we have observed several hundred times in several computational method works.

Ensemble classification based feature selection: a case of identification on plant pentatricopeptide repeat proteins

Briefings in Bioinformatics

Zhai

Liu

et al. 2022

In order to identify plant pentatricopeptide repeat (PPR) proteins, a framework of variable selection has been proposed. In fact, it is an effective feature selection strategy that focuses on the performance of classification. Random forest has been used as the classifier with certain variables automatically selected for discrimination between PPR functional and non-functional proteins. However, it is found that samples regarded as PPR functional proteins are wrongly classified in a high rate. In this paper, we plan to improve the framework in order to achieve better classification results. Modifications are made on the framework for better identifying PPR functional proteins. Instead of random forest, a hybrid ensemble classifier is built with its base classifiers derived from six different classification methods. Besides, an incremental strategy and a clustering by search in descending order are alternatively used for feature selection, which can effectively select the most representative variables for identification on PPR proteins. In addition, it can be found that different base classifiers alternately play an important role in the ensemble classifier with feature dimension increasing. The experimental results demonstrate the effectiveness of our improvements.