Feature selection using Joint Mutual Information Maximisation

Bennasar, Mohamed; Hicks, Yulia; Setchi, Rossitza

doi:10.1016/j.eswa.2015.07.007

Cited by 538 publications

(299 citation statements)

References 27 publications

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“…Multi-information can be positive, negative, or zero [30]. If the multi-information value is zero, the random variables are independent in the context of the third variable.…”

Section: Entropy and Mutual Informationmentioning

confidence: 99%

Feature Selection and Overlapping Clustering-Based Multilabel Classification Model

Liwen

Liu

2018

Mathematical Problems in Engineering

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Multilabel classification (MLC) learning, which is widely applied in real-world applications, is a very important problem in machine learning. Some studies show that a clustering-based MLC framework performs effectively compared to a nonclustering framework. In this paper, we explore the clustering-based MLC problem. Multilabel feature selection also plays an important role in classification learning because many redundant and irrelevant features can degrade performance and a good feature selection algorithm can reduce computational complexity and improve classification accuracy. In this study, we consider feature dependence and feature interaction simultaneously, and we propose a multilabel feature selection algorithm as a preprocessing stage before MLC. Typically, existing cluster-based MLC frameworks employ a hard cluster method. In practice, the instances of multilabel datasets are distinguished in a single cluster by such frameworks; however, the overlapping nature of multilabel instances is such that, in real-life applications, instances may not belong to only a single class. Therefore, we propose a MLC model that combines feature selection with an overlapping clustering algorithm. Experimental results demonstrate that various clustering algorithms show different performance for MLC, and the proposed overlapping clustering-based MLC model may be more suitable.

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“…Multi-information can be positive, negative, or zero [30]. If the multi-information value is zero, the random variables are independent in the context of the third variable.…”

Section: Entropy and Mutual Informationmentioning

confidence: 99%

Feature Selection and Overlapping Clustering-Based Multilabel Classification Model

Liwen

Liu

2018

Mathematical Problems in Engineering

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“…The Venn diagram, a presentation of concepts of H, MI, CMI JMI and II, is provided for visual explanation of each method, as Figure 2 shows [46]. As shown in Figure 2, the value of H represents the variable uncertainty, which also means the information contained in this variable.…”

Section: Mutual Informationmentioning

confidence: 99%

“…The Venn diagram, a presentation of concepts of H, MI, CMI JMI and II, is provided for visual explanation of each method, as Figure 2 shows [46]. Thus, MI expressing the information shared in both variables is defined as Equation (28):…”

Section: Mutual Informationmentioning

confidence: 99%

Correlation Feature Selection and Mutual Information Theory Based Quantitative Research on Meteorological Impact Factors of Module Temperature for Solar Photovoltaic Systems

Sun

Wang

et al. 2016

Energies

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Abstract:The module temperature is the most important parameter influencing the output power of solar photovoltaic (PV) systems, aside from solar irradiance. In this paper, we focus on the interdisciplinary research that combines the correlation analysis, mutual information (MI) and heat transfer theory, which aims to figure out the correlative relations between different meteorological impact factors (MIFs) and PV module temperature from both quality and quantitative aspects. The identification and confirmation of primary MIFs of PV module temperature are investigated as the first step of this research from the perspective of physical meaning and mathematical analysis about electrical performance and thermal characteristic of PV modules based on PV effect and heat transfer theory. Furthermore, the quantitative description of the MIFs influence on PV module temperature is mathematically formulated as several indexes using correlation-based feature selection (CFS) and MI theory to explore the specific impact degrees under four different typical weather statuses named general weather classes (GWCs). Case studies for the proposed methods were conducted using actual measurement data of a 500 kW grid-connected solar PV plant in China. The results not only verified the knowledge about the main MIFs of PV module temperatures, more importantly, but also provide the specific ratio of quantitative impact degrees of these three MIFs respectively through CFS and MI based measures under four different GWCs.

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“…It is employs joint mutual information and the 'maximum of the minimum' choose the most relevant features according the follow criterion (Bennasar et al, 2015):…”

Section: Related Workmentioning

confidence: 99%

RJMIM: A New Feature Selection Method Based On Joint Mutual Information

2016

Revista de la Facultad de Ingeniería

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Feature selection based on information theory plays an important role in classification algorithm due to its computational efficiency and independent from classification method. It is widely used in many application areas like data mining, bioinformatics and machine learning. But drawbacks of these methods are the neglect of the feature interaction and overestimation of features significance due to the limitations of goal functions criterion. To address this problem, we proposed a new feature goal function RJMIM. The method employed joint mutual information and information interaction, which alleviates the shortcomings of overestimation of the feature significance as demonstrated both theoretically and experimentally. The experiments conducted to verify the performance of the proposed method, it compared with four well-known feature selection methods use three publically available datasets from UCI. The average classification accuracy and C4.5 classifier is used to assess the effectiveness of RJMIM method.

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Feature selection using Joint Mutual Information Maximisation

Cited by 538 publications

References 27 publications

Feature Selection and Overlapping Clustering-Based Multilabel Classification Model

Feature Selection and Overlapping Clustering-Based Multilabel Classification Model

Correlation Feature Selection and Mutual Information Theory Based Quantitative Research on Meteorological Impact Factors of Module Temperature for Solar Photovoltaic Systems

RJMIM: A New Feature Selection Method Based On Joint Mutual Information

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