Graph-based Extreme Feature Selection for Multi-class Classification Tasks
When processing high-dimensional datasets, a common pre-processing step is feature selection. Filter-based feature selection algorithms are not tailored to a specific classification method, but rather rank the relevance of each feature with respect to the target and the task. This work focuses on a graph-based, filter feature selection method that is suited for multi-class classifications tasks. We aim to drastically reduce the number of selected features, in order to create a sketch of the original data that codes valuable information for the classification task. The proposed graph-based algorithm is constructed by combing the Jeffries-Matusita distance with a non-linear dimension reduction method, diffusion maps. Feature elimination is performed based on the distribution of the features in the low-dimensional space. Then, a very small number of feature that have complementary separation strengths, are selected. Moreover, the low-dimensional embedding allows to visualize the feature space. Experimental results are provided for public datasets and compared with known filter-based feature selection techniques.
Code (0)
등록된 구현이 없습니다.
Tasks
Dimensionality Reductionfeature selectionMulti-class ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Choosing a Classical Planner with Graph Neural Networks
Online planner selection is the task of choosing a solver out of a predefined set for a given planning problem. As planning is computationally hard, the performance of solvers varies greatly on planning problems. Thus, t…
MinShap: A Modified Shapley Value Approach for Feature Selection
Feature selection is a classical problem in statistics and machine learning, and it continues to remain an extremely challenging problem especially in the context of unknown non-linear relationships with dependent featur…
Accelerating Extreme Classification via Adaptive Feature Agglomeration
Extreme classification seeks to assign each data point, the most relevant labels from a universe of a million or more labels. This task is faced with the dual challenge of high precision and scalability, with millisecond…
ClassificationClusteringGeneral ClassificationExtreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic Graphs
Detecting anomalies in a temporal sequence of graphs can be applied is areas such as the detection of accidents in transport networks and cyber attacks in computer networks. Existing methods for detecting abnormal graphs…
Anomaly DetectionTime SeriesTime Series AnalysisA novel feature selection method based on quantum support vector machine
Feature selection is critical in machine learning to reduce dimensionality and improve model accuracy and efficiency. The exponential growth in feature space dimensionality for modern datasets directly results in ambiguo…
feature selectionQuantum Machine Learning