363 research outputs found
Multi-objective Feature Selection in Remote Health Monitoring Applications
Radio frequency (RF) signals have facilitated the development of non-contact
human monitoring tasks, such as vital signs measurement, activity recognition,
and user identification. In some specific scenarios, an RF signal analysis
framework may prioritize the performance of one task over that of others. In
response to this requirement, we employ a multi-objective optimization approach
inspired by biological principles to select discriminative features that
enhance the accuracy of breathing patterns recognition while simultaneously
impeding the identification of individual users. This approach is validated
using a novel vital signs dataset consisting of 50 subjects engaged in four
distinct breathing patterns. Our findings indicate a remarkable result: a
substantial divergence in accuracy between breathing recognition and user
identification. As a complementary viewpoint, we present a contrariwise result
to maximize user identification accuracy and minimize the system's capacity for
breathing activity recognition.Comment: Under revie
Shift-based density estimation for pareto-based algorithms in many-objective optimization
It is commonly accepted that Pareto-based evolutionary multiobjective optimization (EMO) algorithms encounter difficulties in dealing with many-objective problems. In these algorithms, the ineffectiveness of the Pareto dominance relation for a high-dimensional space leads diversity maintenance mechanisms to play the leading role during the evolutionary process, while the preference of diversity maintenance mechanisms for individuals in sparse regions results in the final solutions distributed widely over the objective space but distant from the desired Pareto front. Intuitively, there are two ways to address this problem: 1) modifying the Pareto dominance relation and 2) modifying the diversity maintenance mechanism in the algorithm. In this paper, we focus on the latter and propose a shift-based density estimation (SDE) strategy. The aim of our study is to develop a general modification of density estimation in order to make Pareto-based algorithms suitable for many-objective optimization. In contrast to traditional density estimation that only involves the distribution of individuals in the population, SDE covers both the distribution and convergence information of individuals. The application of SDE in three popular Pareto-based algorithms demonstrates its usefulness in handling many-objective problems. Moreover, an extensive comparison with five state-of-the-art EMO algorithms reveals its competitiveness in balancing convergence and diversity of solutions. These findings not only show that SDE is a good alternative to tackle many-objective problems, but also present a general extension of Pareto-based algorithms in many-objective optimization. © 2013 IEEE
Multi-objective genetic programming with partial sampling and its extension to many-objective
This paper describes a technique on an optimization of tree-structure data by of multi-objective evolutionary algorithm, or multi-objective genetic programming. GP induces bloat of the tree structure as one of the major problem. The cause of bloat is that the tree structure obtained by the crossover operator grows bigger and bigger but its evaluation does not improve. To avoid the risk of bloat, a partial sampling operator is proposed as a mating operator. The size of the tree and a structural distance are introduced into the measure of the tree-structure data as the objective functions in addition to the index of the goodness of tree structure. GP is defined as a three-objective optimization problem. SD is also applied for the ranking of parent individuals instead to the crowding distance of the conventional NSGA-II. When the index of the goodness of tree-structure data is two or more, the number of objective functions in the above problem becomes four or more. We also propose an effective many-objective EA applicable to such the many-objective GP. We focus on NSGA-II based on Pareto partial dominance (NSGA-II-PPD). NSGA-II-PPD requires beforehand a combination list of the number of objective functions to be used for Pareto partial dominance (PPD). The contents of the combination list greatly influence the optimization result. We propose to schedule a parameter r meaning the subset size of objective functions for PPD and to eliminate individuals created by the mating having the same contents as the individual of the archive set
Compact NSGA-II for Multi-objective Feature Selection
Feature selection is an expensive challenging task in machine learning and
data mining aimed at removing irrelevant and redundant features. This
contributes to an improvement in classification accuracy, as well as the budget
and memory requirements for classification, or any other post-processing task
conducted after feature selection. In this regard, we define feature selection
as a multi-objective binary optimization task with the objectives of maximizing
classification accuracy and minimizing the number of selected features. In
order to select optimal features, we have proposed a binary Compact NSGA-II
(CNSGA-II) algorithm. Compactness represents the population as a probability
distribution to enhance evolutionary algorithms not only to be more
memory-efficient but also to reduce the number of fitness evaluations. Instead
of holding two populations during the optimization process, our proposed method
uses several Probability Vectors (PVs) to generate new individuals. Each PV
efficiently explores a region of the search space to find non-dominated
solutions instead of generating candidate solutions from a small population as
is the common approach in most evolutionary algorithms. To the best of our
knowledge, this is the first compact multi-objective algorithm proposed for
feature selection. The reported results for expensive optimization cases with a
limited budget on five datasets show that the CNSGA-II performs more
efficiently than the well-known NSGA-II method in terms of the hypervolume (HV)
performance metric requiring less memory. The proposed method and experimental
results are explained and analyzed in detail.Comment: 8 pages, 2 figure
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