Dataset Information

Feature Selection based on the Local Lift Dependence Scale.

ABSTRACT: This paper uses a classical approach to feature selection: minimization of a cost function applied on estimated joint distributions. However, in this new formulation, the optimization search space is extended. The original search space is the Boolean lattice of features sets (BLFS), while the extended one is a collection of Boolean lattices of ordered pairs (CBLOP), that is (features, associated value), indexed by the elements of the BLFS. In this approach, we may not only select the features that are most related to a variable Y, but also select the values of the features that most influence the variable or that are most prone to have a specific value of Y. A local formulation of Shannon's mutual information, which generalizes Shannon's original definition, is applied on a CBLOP to generate a multiple resolution scale for characterizing variable dependence, the Local Lift Dependence Scale (LLDS). The main contribution of this paper is to define and apply the LLDS to analyse local properties of joint distributions that are neglected by the classical Shannon's global measure in order to select features. This approach is applied to select features based on the dependence between: i-the performance of students on university entrance exams and on courses of their first semester in the university; ii-the congress representative party and his vote on different matters; iii-the cover type of terrains and several terrain properties.

SUBMITTER: Marcondes D

PROVIDER: S-EPMC7512664 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Feature Selection based on the Local Lift Dependence Scale.

Marcondes Diego D Simonis Adilson A Barrera Junior J

Entropy (Basel, Switzerland) 20180130 2

This paper uses a classical approach to feature selection: minimization of a cost function applied on estimated joint distributions. However, in this new formulation, the optimization search space is extended. The original search space is the Boolean lattice of features sets (BLFS), while the extended one is a collection of Boolean lattices of ordered pairs (CBLOP), that is (features, associated value), indexed by the elements of the BLFS. In this approach, we may not only select the features th ...[more]

PMID: 33265188

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Project description:In recent years, functional brain network topological features have been widely used as classification features. Previous studies have found that network node scale differences caused by different network parcellation definitions significantly affect the structure of the constructed network and its topological properties. However, we still do not know how network scale differences affect the classification accuracy, performance of classification features, and effectiveness of the feature selection strategy using P values in terms of the machine learning method. This study used five scale parcellations, involving 90, 256, 497, 1003, and 1501 nodes. Three local properties of resting-state functional brain networks were selected (degree, betweenness centrality, and nodal efficiency), and the support vector machine method was used to construct classifiers to identify patients with major depressive disorder. We analyzed the impact of the five scales on classification accuracy. In addition, the effectiveness and redundancy of features obtained by the different scale parcellations were compared. Finally, traditional statistical significance (P value) was verified as a feature selection criterion. The results showed that the feature effectiveness of different scales was similar; in other words, parcellation with more regions did not provide more effective discriminative features. Nevertheless, parcellation with more regions did provide a greater quantity of discriminative features, which led to an improvement in the accuracy of the classification. However, due to the close distance between brain regions, the redundancy of parcellation with more regions was also greater. The traditional P value feature selection strategy is feasible with different scales, but our analysis showed that the traditional P < 0.05 threshold was too strict for feature selection. This study provides an important reference for the selection of network scales when applying topological properties of brain networks to machine learning methods.

Dataset Information

Feature Selection based on the Local Lift Dependence Scale.

Publications

Feature Selection based on the Local Lift Dependence Scale.

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