Velocity Bounded Boolean Particle Swarm Optimization for improved feature selection in liver and kidney disease diagnosis

Gunasundari, S.; Janakiraman, S.; Meenambal, S.

doi:10.1016/j.eswa.2016.02.042

Cited by 75 publications

(33 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The learning approach employed in most of the PSO based feature selection methods can be classified into three categories: (1) euclidean distance based [17] (2) probabilistic [19] and (3) Boolean logic based [30]. In the canonical PSO [17], euclidean distance from the learning exemplar is used as a learning, as illustrated by the following velocity update rule for the d th dimension of the i th particle,…”

Section: Feature Selection Through Psomentioning

confidence: 99%

A two-dimensional (2-D) learning framework for Particle Swarm based feature selection

Hafiz

Swain

Patel

et al. 2018

Pattern Recognition

View full text Add to dashboard Cite

This paper proposes a new generalized two dimensional learning approach for particle swarm based feature selection. The core idea of the proposed approach is to include the information about the subset cardinality into the learning framework by extending the dimension of the velocity. The 2D-learning framework retains all the key features of the original PSO, despite the extra learning dimension. Most of the popular variants of PSO can easily be adapted into this 2D learning framework for feature selection problems. The efficacy of the proposed learning approach has been evaluated considering several benchmark data and two induction algorithms: Naive-Bayes and k-Nearest Neighbor. The results of the comparative investigation including the time-complexity analysis with GA, ACO and five other PSO variants illustrate that the proposed 2D learning approach gives feature subset with relatively smaller cardinality and better classification performance with shorter run times.

show abstract

Section: Feature Selection Through Psomentioning

confidence: 99%

A two-dimensional (2-D) learning framework for Particle Swarm based feature selection

Hafiz

Swain

Patel

et al. 2018

Pattern Recognition

View full text Add to dashboard Cite

show abstract

“…They showed that their proposed method performs better than GA and PCA. Some researchers have also used particle swarm optimization (Gunasundari et al, 2016;Xue et al, 2014) and ant colony optimization (Ali Jan Ghasab et al, 2015) to deal with the feature selection problem. These research studies either used the accuracy rate or used a weighted sum of the accuracy rate and the number of selected features for evaluating the performance of the individuals in the population.…”

Section: Review Of Feature Selection Methodsmentioning

confidence: 99%

“…In the first stage, we deal with the problem of identifying subsets of important features, which will be used as inputs to the second stage for building a stacking model. In order to perform feature selection, researchers have applied several metaheuristic algorithms, including ant colony optimization (Ali Jan Ghasab et al, 2015;Goodarzi et al, 2009), particle swarm optimization (Gunasundari et al, 2016;Xue et al, 2014), and genetic algorithm (Garc ıa-Nieto et al, 2009;Jiang et al, 2017;Tsai and Hsiao, 2010;Urraca et al, 2015). We have chosen the genetic algorithm as the search strategy for feature selection since it is known for its capability in exploring solution space in combinatorial optimization problems (Gen and Cheng, 2000).…”

Section: Introductionmentioning

confidence: 99%

A metaheuristic-based stacking model for predicting the risk of patient no-show and late cancellation for neurology appointments

Ahmadi

Garcia-Arce

Masel

et al. 2019

IISE Transactions on Healthcare Systems Engineering

View full text Add to dashboard Cite

Patient no-shows and late cancellations for an appointment are common problems in healthcare, which adversely affect the financial performance and quality of service of healthcare organizations. A high rate of patient no-show and late cancellation in a clinic can significantly limit access to healthcare. In general, hospitals create predictive models to assess risk of no-show, and then assign overbooking appointments utilizing those risks. In this paper, by incorporating machine learning and optimization techniques, we proposed a predictive model to assist with the overbooking decision. The model consists of two phases. First, we utilized a metaheuristic optimization technique to explore the best subset of featuresknown as feature selection problemthat can significantly contribute to the prediction outcomes. Second, using the output of the first stage, we proposed a stacking model to improve the prediction performances further. Our extensive computations and comparisons across different classifiers show that formulating the feature selection problem as a multi-objective problem instead of a single-objective problem using random forest classifier yields better results. The proposed model will improve the overbooking at clinics, by increasing the patient access to care. We introduced important new features to the literature that can describe the no-show and late cancellation behavior.

show abstract

“…This way, the pbest becomes, or nearly is the same as gbest. This, in turn, will lead to premature convergence and low diversity [16,35]. To resolve this issue, a pbest guide strategy is introduced.…”

Section: Velocity and Position Updatementioning

confidence: 99%

EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization

et al. 2019

View full text Add to dashboard Cite

Due to the increment in hand motion types, electromyography (EMG) features are increasingly required for accurate EMG signals classification. However, increasing in the number of EMG features not only degrades classification performance, but also increases the complexity of the classifier. Feature selection is an effective process for eliminating redundant and irrelevant features. In this paper, we propose a new personal best (Pbest) guide binary particle swarm optimization (PBPSO) to solve the feature selection problem for EMG signal classification. First, the discrete wavelet transform (DWT) decomposes the signal into multiresolution coefficients. The features are then extracted from each coefficient to form the feature vector. After which pbest-guide binary particle swarm optimization (PBPSO) is used to evaluate the most informative features from the original feature set. In order to measure the effectiveness of PBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA), modified binary tree growth algorithm (MBTGA), and binary differential evolution (BDE) were used for performance comparison. Our experimental results show the superiority of PBPSO over other methods, especially in feature reduction; where it can reduce more than 90% of features while keeping a very high classification accuracy. Hence, PBPSO is more appropriate for application in clinical and rehabilitation applications.

show abstract

Velocity Bounded Boolean Particle Swarm Optimization for improved feature selection in liver and kidney disease diagnosis

Cited by 75 publications

References 46 publications

A two-dimensional (2-D) learning framework for Particle Swarm based feature selection

A two-dimensional (2-D) learning framework for Particle Swarm based feature selection

A metaheuristic-based stacking model for predicting the risk of patient no-show and late cancellation for neurology appointments

EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization

Contact Info

Product

Resources

About