A survey of nature-inspired algorithms for feature selection to identify Parkinson's disease
TL;DR: A comparative analysis of various nature inspired algorithms to select optimal features/variables required for aiding in the classification of affected patients from the rest shows Binary Bat Algorithm outperformed traditional techniques like Particle Swarm Optimization (PSO), Genetic Algorithm and Modified Cuckoo Search Algorithm with a competitive recognition rate on the dataset of selected features.
About: This article is published in Computer Methods and Programs in Biomedicine.The article was published on 2017-02-01. It has received 70 citations till now. The article focuses on the topics: Bat algorithm & Cuckoo search.
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TL;DR: In this paper, the authors present a taxonomy of nature-inspired and bio-inspired algorithms, and provide a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper.
Abstract: In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.
109 citations
TL;DR: A multi-objective PSO based method named RFPSOFS that ranks the features based on their frequencies in the archive set that improves the performance of model, decreasing the computational cost, and adjusting the "curse of dimensionality" is proposed.
Abstract: Feature selection is an important preprocessing task in classification that eliminates the irrelevant, redundant, and noisy features Improving the performance of model, decreasing the computational cost, and adjusting the “curse of dimensionality” are the key advantages of feature selection task The evolution process of the existing multi-objective based feature selection algorithms is relied on the objective space while the problem space contains useful information This paper proposes a multi-objective PSO based method named RFPSOFS that ranks the features based on their frequencies in the archive set Then, these ranks are used to refine the archive set and guide the particles The proposed method is compared with three PSO based and one genetic based multi-objective methods on 9 Benchmark datasets Qualitative and quantitative analyses of the results are performed by visual analysis of the Pareto fronts and three performance metrics respectively Finally, remarkable performance in datasets with more than hundred features and satisfactory performance in datasets with less than hundred features are obtained
92 citations
TL;DR: The proposed wavelet transform based representation of spatiotemporal gait variables can efficiently extract relevant features from the different levels of the wavelet towards the classification of Parkinson's and healthy subjects and thus, the present work is a potential candidate for the automatic noninvasive neurodegenerative disease classification.
92 citations
TL;DR: This is the first approach developed to properly consider intra-subject variability for variable selection and classification and it can be applied in other contexts with similar replication-based experimental designs.
86 citations
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TL;DR: A novel approach for estimation of temporal association pattern prevalence values and a novel temporal fuzzy similarity measure which holds monotonicity to find similarity between any two temporal patterns are proposed.
86 citations
References
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01 Dec 2009
TL;DR: A new meta-heuristic algorithm, called Cuckoo Search (CS), is formulated, based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Lévy flight behaviour ofSome birds and fruit flies, for solving optimization problems.
Abstract: In this paper, we intend to formulate a new meta-heuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Levy flight behaviour of some birds and fruit flies. We validate the proposed algorithm against test functions and then compare its performance with those of genetic algorithms and particle swarm optimization. Finally, we discuss the implication of the results and suggestion for further research.
5,521 citations
TL;DR: This paper presents a more extensive comparison study using some standard test functions and newly designed stochastic test functions to apply the CS algorithm to solve engineering design optimisation problems, including the design of springs and welded beam structures.
Abstract: A new metaheuristic optimisation algorithm, called cuckoo search (CS), was developed recently by Yang and Deb (2009). This paper presents a more extensive comparison study using some standard test functions and newly designed stochastic test functions. We then apply the CS algorithm to solve engineering design optimisation problems, including the design of springs and welded beam structures. The optimal solutions obtained by CS are far better than the best solutions obtained by an efficient particle swarm optimiser. We will discuss the unique search features used in CS and the implications for further research.
1,339 citations
TL;DR: A new nature‐inspired metaheuristic optimization algorithm, called bat algorithm (BA), based on the echolocation behavior of bats is introduced, and the optimal solutions obtained are better than the best solutions obtained by the existing methods.
Abstract: – Nature‐inspired algorithms are among the most powerful algorithms for optimization. The purpose of this paper is to introduce a new nature‐inspired metaheuristic optimization algorithm, called bat algorithm (BA), for solving engineering optimization tasks., – The proposed BA is based on the echolocation behavior of bats. After a detailed formulation and explanation of its implementation, BA is verified using eight nonlinear engineering optimization problems reported in the specialized literature., – BA has been carefully implemented and carried out optimization for eight well‐known optimization tasks; then a comparison has been made between the proposed algorithm and other existing algorithms., – The optimal solutions obtained by the proposed algorithm are better than the best solutions obtained by the existing methods. The unique search features used in BA are analyzed, and their implications for future research are also discussed in detail.
1,316 citations
TL;DR: The UPDRS is a multidimensional, reliable, and valid scale, with some inconveniences derived from its internal consistency, discriminant validity, and pragmatic application.
Abstract: Our purpose was to verify some basic aspects of validation of the Unified Parkinson's Disease Rating Scale (UPDRS). One hundred and sixty-seven Parkinson's disease (PD) patients were included. Group A (n = 40) was simultaneously assessed by five raters who applied the UPDRS and other PD rating scales (PDRS). A set of timed tests, the Mini-Mental State Examination (MMSE), and the Hamilton Scale for Depression (HSD) were administered by an independent examiner. Group B (n = 127) was individually assessed through the UPDRS and the other PDRSs by one neurologist in four different hospitals. The UPDRS was administered in 16.95 +/- 7.98 min. The internal consistency was high (Cronbach's alpha = 0.96). Nevertheless, the items related to depression, motivation/initiative, and tremor were scarcely consistent. The Interrater reliability was satisfactory (all the items had k > 0.40). There was a high correlation of the UPDRS with the Hoehn and Yahr staging (rs = 0.71; p < 0.001) and some timed tests (finger tapping; arising from chair), but also with the MMSE and HSD (rs = 0.53; rs = 0.64; p < 0.001). The convergent validity with the other PDRS (Intermediate Scale and Schwab and England Scale) was very high (rs = 0.76-0.96; p < 0.001). The factor analysis identified six factors that explained 59.6% of the variance. The dimension "tremor" showed a remarkable independence. The UPDRS is a multidimensional, reliable, and valid scale, with some inconveniences derived from its internal consistency, discriminant validity, and pragmatic application.
742 citations
TL;DR: It is demonstrated that regulation of gait variability and rhythmicity is apparently an automatic process that does not demand attention in healthy adults, however, in patients with PD this ability becomes attention‐demanding and worsens when subjects perform secondary tasks.
Abstract: Cognitive function and the performance of a secondary, dual task may affect certain aspects of gait, but the relationships between cognitive function and gait are not well understood. To better understand the motor control of gait and the relationship between cognitive function and gait, we studied cognitive function and the effects of different types of dual tasking on the gait of patients with Parkinson’s disease (PD) and controls, contrasting measures of gait automaticity and rhythmicity with other features. Patients with idiopathic PD (n ¼ 30; mean age 71.8 year) with moderate disease severity (Hoehn and Yahr Stage 2–3) were compared to age and gendermatched healthy controls (n ¼ 28). Memory and executive function were also assessed. In both groups, gait speed decreased in response to dual tasking, in a parallel fashion. For the PD group only, gait variability increased compared to usual walking. Executive function was significantly worse in the PD group, while memory was not different in the two groups. Executive function measures were significantly correlated with gait variability during dual tasking, but not during usual walking. These findings demonstrate that regulation of gait variability and rhythmicity is apparently an automatic process that does not demand attention in healthy adults. In patients with PD, however, this ability becomes attention-demanding and worsens when subjects perform secondary tasks. Moreover, the associations between executive function and gait variability suggest that a decline in executive function in PD may exacerbate the effects of dual tasking on gait, potentially increasing fall risk.
729 citations