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A data-science pipeline to enable the Interpretability of Many-Objective Feature Selection
Machine Learning · Submitted 30 Nov 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The results show the added value of the methodology in the selection of the final subset of features.
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The abstract, sentence by sentence
Many-Objective Feature Selection (MOFS) approaches use four or more objectives to determine the relevance of a subset of features in a supervised learning task. As a consequence, MOFS typically returns a large set of non-dominated solutions, which have to be assessed by the data scientist in order to proceed with the final choice. Given the multi-variate nature of the assessment, which may include criteria (e.g. fairness) not related to predictive accuracy, this step is often not straightforward and suffers from the lack of existing tools. For instance, it is common to make use of a tabular presentation of the solutions, which provide little information about the trade-offs and the relations between criteria over the set of solutions. This paper proposes an original methodology to support data scientists in the interpretation and comparison of the MOFS outcome by combining post-processing and visualisation of the set of solutions. The methodology supports the data scientist in the selection of an optimal feature subset by providing her with high-level information at three different levels: objectives, solutions, and individual features. The methodology is experimentally assessed on two feature selection tasks adopting a GA-based MOFS with six objectives (number of selected features, balanced accuracy, F1-Score, variance inflation factor, statistical parity, and equalised odds). The results show the added value of the methodology in the selection of the final subset of features.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Many-Objective Feature Selection (MOFS) approaches use four or more objectives to determine the relevance of a subset of features in a supervised learning task. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | As a consequence, MOFS typically returns a large set of non-dominated solutions, which have to be assessed by the data scientist in order to proceed with the final choice. | background 0.97 | 0.08 | 0.02 | asserted 0.93 |
| s2 | Given the multi-variate nature of the assessment, which may include criteria (e.g. fairness) not related to predictive accuracy, this step is often not straightforward and suffers from the lack of existing tools. | gap 0.77 | 0.06 | 0.02 | asserted 0.61 |
| s3 | For instance, it is common to make use of a tabular presentation of the solutions, which provide little information about the trade-offs and the relations between criteria over the set of solutions. | background 0.62 | 0.08 | 0.02 | asserted 0.83 |
| s4 | This paper proposes an original methodology to support data scientists in the interpretation and comparison of the MOFS outcome by combining post-processing and visualisation of the set of solutions. | contribution 1.00 | 0.82 | 0.19 | asserted 0.99 |
| s5 | The methodology supports the data scientist in the selection of an optimal feature subset by providing her with high-level information at three different levels: objectives, solutions, and individual features. | method 0.55 | 0.77 | 0.15 | asserted 1.00 |
| s6 | The methodology is experimentally assessed on two feature selection tasks adopting a GA-based MOFS with six objectives (number of selected features, balanced accuracy, F1-Score, variance inflation factor, statistical parity, and equalised odds). | method 0.97 | 0.95 | 0.07 | asserted 0.94 |
| s7 | The results show the added value of the methodology in the selection of the final subset of features. | result 0.82 | 0.94 | 0.83 | asserted 1.00 |
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The paper
Original abstract
Many-Objective Feature Selection (MOFS) approaches use four or more objectives to determine the relevance of a subset of features in a supervised learning task. As a consequence, MOFS typically returns a large set of non-dominated solutions, which have to be assessed by the data scientist in order to proceed with the final choice. Given the multi-variate nature of the assessment, which may include criteria (e.g. fairness) not related to predictive accuracy, this step is often not straightforward and suffers from the lack of existing tools. For instance, it is common to make use of a tabular presentation of the solutions, which provide little information about the trade-offs and the relations between criteria over the set of solutions. This paper proposes an original methodology to support data scientists in the interpretation and comparison of the MOFS outcome by combining post-processing and visualisation of the set of solutions. The methodology supports the data scientist in the selection of an optimal feature subset by providing her with high-level information at three different levels: objectives, solutions, and individual features. The methodology is experimentally assessed on two feature selection tasks adopting a GA-based MOFS with six objectives (number of selected features, balanced accuracy, F1-Score, variance inflation factor, statistical parity, and equalised odds). The results show the added value of the methodology in the selection of the final subset of features.
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