Multi-Label Feature Selection Based on High-Order Label Correlation Assumption

Abstract

Multi-label data often involve features with high dimensionality and complicated label correlations, resulting in a great challenge for multi-label learning. Feature selection plays an important role in multi-label learning to address multi-label data. Exploring label correlations is crucial for multi-label feature selection. Previous information-theoretical-based methods employ the strategy of cumulative summation approximation to evaluate candidate features, which merely considers low-order label correlations. In fact, there exist high-order label correlations in label set, labels naturally cluster into several groups, similar labels intend to cluster into the same group, different labels belong to different groups. However, the strategy of cumulative summation approximation tends to select the features related to the groups containing more labels while ignoring the classification information of groups containing less labels. Therefore, many features related to similar labels are selected, which leads to poor classification performance. To this end, Max-Correlation term considering high-order label correlations is proposed. Additionally, we combine the Max-Correlation term with feature redundancy term to ensure that selected features are relevant to different label groups. Finally, a new method named Multi-label Feature Selection considering Max-Correlation (MCMFS) is proposed. Experimental results demonstrate the classification superiority of MCMFS in comparison to eight state-of-the-art multi-label feature selection methods.

Parallel Dual-channel Multi-label Feature Selection

“…The proposed algorithm PDMFS is compared with five feature selection algorithms, namely Multi-Label Dimensionality Reduction via Dependence Maximization (MDDMspc, MDDMproj) [27], Feature Selection for Multi-label Classification Using Multivariate Mutual Information (PMU) [26], Multi-label Feature Selection algorithm based on Neighborhood Mutual Information (MFNMIpes) [31], Multi-label feature selection using multi-criteria decision making (MFS-MCDM) [28], Multi-Label Feature Selection Considering the Max-Correlation in high-order label (MCMFS) [11]. PMU, MDDMspc, MDDMproj, MFNMIpes and as classical algorithms for information metrics in feature selection are used to compare the effectiveness of PDMFS performance.…”

Multi-Label Feature Selection Combining Three Types of Conditional Relevance

“…The example-based evaluation criteria include the Hamming Loss (HL) and Zero One Loss (ZOL) [ 33 ]. The lower the value of these two indicators, the better the classification effect.…”

“…Problem transform is the conversion of multi-label learning into traditional single-label learning, such as Binary Relevance (BR) [ 36 ], Pruned Problem Transformation (PPT) [ 37 ], and Label Power (LP) [ 38 ]. BR treats the prediction of each label as an independent single classification issue and trains an individual classifier for each label with all of the training data [ 33 ]. However, it ignores the relationships between the labels, so it is possible to end up with imbalanced data.…”

An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification

“…It should however be noted that most of these research works focus on a limited/specific number of species. The post processing i.e., after the microbe segmentation, the segmented images are subjected to the feature extraction [ 26 ] and selection [ 27 ] process. Recent developments [ 22 , 28 , 29 ] are based on extracting morphological features for building the identification system.…”