A survey of feature selection and feature extraction techniques in machine learning

Khalid, Samina; Khalil, Tehmina; Nasreen, Shamila

doi:10.1109/sai.2014.6918213

Cited by 896 publications

(539 citation statements)

References 17 publications

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“…Feature redundancy increases search space size and affects the speed and the accuracy of learning algorithms [49]. The correlation coefficient, which indicates the strength and direction of a relationship between two random variables, is used to measure the redundancy.…”

Section: Evaluation Of Feature Redundancymentioning

confidence: 99%

Feature Selection Solution with High Dimensionality and Low-Sample Size for Land Cover Classification in Object-Based Image Analysis

et al. 2017

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Land cover information extraction through object-based image analysis (OBIA) has become an important trend in remote sensing, thanks to the increasing availability of high-resolution imagery. Segmented objects have a large number of features that cause high-dimension and low-sample size problems in the classification process. In this study, on the basis of a partial least squares generalized linear regression (PLSGLR), we propose a group corrected PLSGLR, known as G-PLSGLR, that aims to reduce the redundancy of object features for land cover identifications. Using Gaofen-2 images, the area of interest was segmented and sampled to generate small sample-size training datasets with 51 object features. The features selected by G-PLSGLR were compared against a guided regularized random forest (GRRF) in metrics of reduction rate, feature redundancy, and accuracy assessment of classification. Three indicators of overall accuracy (OA), user's accuracy (UA), and producer's accuracy (PA) were applied for accuracy assessment in this paper. The result shows that the G-PLSGLR achieved a reduction rate of 9.27 with a feature redundancy of 0.29, and a value of OA 90.63%. The GRRF achieved a reduction rate of 1.61 with a feature redundancy of 0.42, and a value of OA 85.56%. The PA of each land cover category was more than 95% using features selected by G-PLSGLR, while the PA ranged from 77 to 96% using features selected by GRRF. The UA of G-PLSGLR-selected features ranged from 70 to 80% except for grass land and bare land, which achieved 10% higher UA than GRRF-selected features. The G-PLSGLR method we proposed has the advantages of a large reduction rate, low feature redundancy, and high classification performance, which can be applied in OBIA-based land cover classification.

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Section: Evaluation Of Feature Redundancymentioning

confidence: 99%

Feature Selection Solution with High Dimensionality and Low-Sample Size for Land Cover Classification in Object-Based Image Analysis

et al. 2017

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“…As the name implies, it is the redundant information, such as duplicate instances and derived attributes of others that contain the same information [65][66]. As mentioned above, the algorithms found in the papers to solve the redundancy issue were classified in four categories: unsupervised learning, supervised learning, statistics and others.…”

Section: Redundancymentioning

confidence: 99%

A systematic review of data quality issues in knowledge discovery tasks

Corrales

Ledezma

Corrales

2016

Rev. ing. univ. Medellín

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Large volume of data is growing because organizations are continuously capturing the collective amount of data for a better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust. ResumenHay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.

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“… Redundancy: as the name implies is the redundant information such as duplicate instances and derived attributes of others that contain the same information [26], [27]. In Table 3 are shown the approaches to solve the problems related to amount of data and redundancy.…”

Section: Journal Of Computersmentioning

confidence: 99%

“…Several approaches exist to tackle the issues of data quality in outliers [22], noise [18], inconsistency [23], incompleteness [24], redundancy [26], [27], amount of data [28]- [30], heterogeneity [14], and timeliness [25]. Nevertheless the results to date not consider resolve the issues in ensemble.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

A Conceptual Framework for Data Quality in Knowledge Discovery Tasks (FDQ-KDT): A Proposal

Corrales¹,

Ledezma²,

Corrales³

2015

JCP

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Large Volume of Data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through data mining and data science methodologies. Nevertheless these not tackle the issues in data quality clearly, leaving out relevant activities. We proposed a conceptual framework for data quality in knowledge discovery tasks based on CRISP-DM, SEMMA and Data Science, considering the issues of ESE Taxonomy.

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A survey of feature selection and feature extraction techniques in machine learning

Cited by 896 publications

References 17 publications

Feature Selection Solution with High Dimensionality and Low-Sample Size for Land Cover Classification in Object-Based Image Analysis

Feature Selection Solution with High Dimensionality and Low-Sample Size for Land Cover Classification in Object-Based Image Analysis

A systematic review of data quality issues in knowledge discovery tasks

A Conceptual Framework for Data Quality in Knowledge Discovery Tasks (FDQ-KDT): A Proposal

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