Maximum-relevance and maximum-diversity of positive ranks: A novel feature selection method

Sheikhi, Ghazaal; Altınçay, Hakan

doi:10.1016/j.eswa.2020.113499

Cited by 6 publications

(5 citation statements)

References 35 publications

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“…This method uses mutual information theory to calculate the final importance value, and the magnitude of the value determines the strength of the relationship where a high score relates to a high relationship and vice versa (Peng et al, 2005). The mRMR method maximises the objective function in Equation (2) (Sheikhi & Altınçay, 2020).

J ((), x_{i}) goodbreak= I (();, x_{i}, y) goodbreak- 1 / (||, S) \sum_{x_{j} ∊ S} I (();, x_{i}, x_{j})

Here

x_{i}

is the i th candidate feature, y is the dependent variable,

I (();, x_{i}, x_{j})

is the mutual information between

x_{i}

and

x_{j}

and S is the feature set excluding the i th candidate feature.…”

Section: Implemented Methodologymentioning

confidence: 99%

Section: Minimum Redundancy Maximum Relevance Methods (Mrmr)mentioning

confidence: 99%

See 1 more Smart Citation

Understanding high‐involvement product purchase through an innovative machine learning approach: A case of housing type choice

Arachchige

Quach

Roca

et al. 2022

J of Consumer Behaviour

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Adopting the conservation of resources theory, this research explores the joint influence of multiple factors affecting the choice of housing, an instance of a high involvement product. We develop an innovative Machine Learning approach to identify the most significant set of factors affecting consumers' housing choices. It was found that housing choices were primarily bound by energy resources and secondarily by other personal, conditional, and object resources. The study identified 25 key factors, many of which previous studies have not explored. The new factors include government payments, vehicle possession, time living with children, marriage duration, current location duration, and health conditions. The results suggest that joint effects of factors are more prominent than individual effects in influencing the choice of high‐involvement products. Further, the study suggests that ML methods are more robust than traditional methods and can be applied to analyse other types of high involvement products. The findings can assist real estate investors, policymakers and other stakeholders to understand sophisticated market behaviours to develop better and tailored housing strategies.

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J ((), x_{i}) goodbreak= I (();, x_{i}, y) goodbreak- 1 / (||, S) \sum_{x_{j} ∊ S} I (();, x_{i}, x_{j})

Here

x_{i}

is the i th candidate feature, y is the dependent variable,

I (();, x_{i}, x_{j})

is the mutual information between

x_{i}

and

x_{j}

and S is the feature set excluding the i th candidate feature.…”

Section: Implemented Methodologymentioning

confidence: 99%

Section: Minimum Redundancy Maximum Relevance Methods (Mrmr)mentioning

confidence: 99%

Understanding high‐involvement product purchase through an innovative machine learning approach: A case of housing type choice

Arachchige

Quach

Roca

et al. 2022

J of Consumer Behaviour

View full text Add to dashboard Cite

show abstract

“…JMIM and its normalized version, NJMIM have been claimed to address these drawbacks at the cost of dismissing the joint mutual information between more than two features which may lead to a suboptimal solution. Recently, a multivariate filter approach named maximum relevance and maximum diversity (MRMD) of positive ranks has been proposed which employs a non MI‐based objective function 15 . MRMD is more effective than its MI‐based counterparts, yet it carries the flaws of greedy search.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, in filter approaches, the evaluation criterion is totally independent from the learning algorithm. In particular, the features are evaluated in terms of their individual discriminative powers and pairwise redundancies 10‐16 . Filter feature selection methods are generally characterized by scalability, low computational complexity, and high levels of generalization 2,8 .…”

Section: Introductionmentioning

confidence: 99%

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A novel dissimilarity metric based on feature‐to‐feature scatter frequencies for clustering‐based feature selection in biomedical data

Sheikhi

Altınçay

2021

Computational Intelligence

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Filter feature selection methods have been extensively used for dimensionality reduction in biomedical data analysis. In this article, a novel dissimilarity metric based on feature‐to‐feature (F2F) scatter frequencies is proposed for clustering‐based filter feature selection. The proposed metric is computed by obtaining the feature‐level ranks of samples and identifying the features which assign close ranks to each sample. The order of ranking is determined for each feature by class labels. Samples are represented as a set of affinity sets containing features having rank differences less than a predefined proximity window size. The F2F dissimilarity of a pair of features is computed using the frequency of their appearance in different affinity sets. Features are then clustered into distinct groups using F2F dissimilarity metric. From each cluster, the feature having the highest relevance score is selected. The experiments conducted on ten biomedical datasets confirmed the effectiveness of the proposed method in improving classification performance. Notably, the proposed method outperforms the widely used schemes in terms of both classification under ROC curve and stability.

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Toward an Autonomous Operation of Wheel Loaders in Collaboration with a Truck for a Construction Site

Hwang,

Park,

Kim

2024

Lecture Notes in Electrical Engineering

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Maximum-relevance and maximum-diversity of positive ranks: A novel feature selection method

Cited by 6 publications

References 35 publications

Understanding high‐involvement product purchase through an innovative machine learning approach: A case of housing type choice

Understanding high‐involvement product purchase through an innovative machine learning approach: A case of housing type choice

A novel dissimilarity metric based on feature‐to‐feature scatter frequencies for clustering‐based feature selection in biomedical data

Toward an Autonomous Operation of Wheel Loaders in Collaboration with a Truck for a Construction Site

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