QMIQPN: An enhanced QPN based on qualitative mutual information for reducing ambiguity

Lv, Yali; Liao, Shizhong; Shi, Hongbo; Qian, Yuhua; Ji, Suqin

doi:10.1016/j.knosys.2014.07.014

Cited by 1 publication

(1 citation statement)

References 28 publications

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“…The entropy, H ( X ), of discrete random variable X with p ( x ) = Pr( X = x ) as its probability density function is defined as follows:

Also, the mutual information (MI) between two random variables X and Y with a joint probability distribution Pr( x , y ) is formulated as follows [ 22 ]:

Hence, Qualitative Mutual Information (QMI) is defined by multiplying a utility function U ( X i , Y j ) to mutual information formula; the formula is as follows [ 23 , 24 ]:

Different informative functions can be applied as the utility function. In the proposed approach, we use the Fisher ratio [ 2 ].…”

Section: Mutual Information Qualitative Mutual Information and Cmentioning

confidence: 99%

Robust Feature Selection from Microarray Data Based on Cooperative Game Theory and Qualitative Mutual Information

Mortazavi

Moattar

2016

Advances in Bioinformatics

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High dimensionality of microarray data sets may lead to low efficiency and overfitting. In this paper, a multiphase cooperative game theoretic feature selection approach is proposed for microarray data classification. In the first phase, due to high dimension of microarray data sets, the features are reduced using one of the two filter-based feature selection methods, namely, mutual information and Fisher ratio. In the second phase, Shapley index is used to evaluate the power of each feature. The main innovation of the proposed approach is to employ Qualitative Mutual Information (QMI) for this purpose. The idea of Qualitative Mutual Information causes the selected features to have more stability and this stability helps to deal with the problem of data imbalance and scarcity. In the third phase, a forward selection scheme is applied which uses a scoring function to weight each feature. The performance of the proposed method is compared with other popular feature selection algorithms such as Fisher ratio, minimum redundancy maximum relevance, and previous works on cooperative game based feature selection. The average classification accuracy on eleven microarray data sets shows that the proposed method improves both average accuracy and average stability compared to other approaches.

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“…The entropy, H ( X ), of discrete random variable X with p ( x ) = Pr( X = x ) as its probability density function is defined as follows:

Also, the mutual information (MI) between two random variables X and Y with a joint probability distribution Pr( x , y ) is formulated as follows [ 22 ]:

Hence, Qualitative Mutual Information (QMI) is defined by multiplying a utility function U ( X i , Y j ) to mutual information formula; the formula is as follows [ 23 , 24 ]:

Different informative functions can be applied as the utility function. In the proposed approach, we use the Fisher ratio [ 2 ].…”

Section: Mutual Information Qualitative Mutual Information and Cmentioning

confidence: 99%

Robust Feature Selection from Microarray Data Based on Cooperative Game Theory and Qualitative Mutual Information

Mortazavi

Moattar

2016

Advances in Bioinformatics

View full text Add to dashboard Cite

show abstract

QMIQPN: An enhanced QPN based on qualitative mutual information for reducing ambiguity

Cited by 1 publication

References 28 publications

Robust Feature Selection from Microarray Data Based on Cooperative Game Theory and Qualitative Mutual Information

Robust Feature Selection from Microarray Data Based on Cooperative Game Theory and Qualitative Mutual Information

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