Differential evolution and ACO based global optimal feature selection with fuzzy rough set for cancer data classification

Meenachi, L.; Ramakrishnan, S.

doi:10.1007/s00500-020-05070-9

Cited by 24 publications

(6 citation statements)

References 36 publications

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“…In the proposed feature selection technique, the run time of the algorithm can be altered by utilizing the tuning parameter 'a' present in the sigmoidal function (37) and (38). Smaller the value of a, lesser the run time of the algorithm.…”

Section: Discussionmentioning

confidence: 99%

Multi-Criteria Decision Making Methods for Ensemble Feature Selection using q-Rung Orthopair Hesitant Fuzzy Distance and Similarity Measures

Kesavan

Seethapathy

Satheeshkumar

et al. 2022

Preprint

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This paper deals with ensemble feature selection using the q-rung orthopair hesitant fuzzy multi-criteria decision-making (MCDM) methods including VIse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Combinative distance-based assessment (CODAS). The novalty of this paper is to design the three MCDM algorithms based on q-rung orthopair hesitant fuzzy sets with different distance and similarity measures. The well known distance and similarity measures are to be taken such as Hausdorff measure, hybrid Hausdorff and distance measure, synergetic measure, similarity for Hausdorff measure, similarity for hybrid Hausdorff and distance measure, similarity for synergetic measure, ordered Hausdorff measure, ordered hybrid Hausdorff and distance measure, similarity for ordered Hausdorff measure and similarity for ordered hybrid Hausdorff and distance measure This is the first time in the literature, an ensemble feature selection problem is modeled as a q-rung orthopair hesitant fuzzy MCDM extended to VIKOR, TOPSIS and CODAS techniques with distance and similarity measures. By using q-ROHFS VIKOR, TOPSIS and CODAS methods, a score is assigned to each feature based on the values of the preference matrix. At last, an output rank vector is produced for all features, from which the user can select the desired number of features. To prove the efficiency and optimality of our proposed method, we compared with the basic filter-based feature selections and ensemble feature selection by using feature ranking strategy. Our method is superior and efficient than the ensemble methods based on the accuracy and F-score levels.

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Section: Discussionmentioning

confidence: 99%

Multi-Criteria Decision Making Methods for Ensemble Feature Selection using q-Rung Orthopair Hesitant Fuzzy Distance and Similarity Measures

Kesavan

Seethapathy

Satheeshkumar

et al. 2022

Preprint

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“…Hence, some hybrid algorithms of ACO and other EC algorithms have been proposed to improve its scalability, such as a hybrid algorithm of ACO and GAs (Hamamoto et al 2015), three mechanisms for hybrid ACO-PSO based approaches for feature selection (Menghour and Souici-Meslati 2016). Recently, Meenachi and Ramakrishnan have hybridized ACO and fuzzy rough set to select global optimal features (Meenachi and Ramakrishnan 2020). Except for the above algorithms, many other EC-based feature selection algorithms have also been developed.…”

Section: Related Workmentioning

confidence: 99%

A self-adaptive level-based learning artificial bee colony algorithm for feature selection on high-dimensional classification

et al. 2022

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Feature selection is an important data preprocessing method in data mining and machine learning, yet it faces the challenge of "curse of dimensionality" when dealing with high-dimensional data. In this paper, a self-adaptive level-based learning artificial bee colony (SLLABC) algorithm is proposed for high-dimensional feature selection problem. The SLLABC algorithm includes three new mechanisms: (1) A novel level-based learning mechanism is introduced to accelerate the convergence of the basic artificial bee colony algorithm, which divides the population into several levels and the individuals on each level learn from the individuals on higher levels, especially, the individuals on the highest level learn from each other. (2) A self-adaptive method is proposed to keep the balance between exploration and exploitation abilities, which takes the diversity of population into account to determine the number of levels. The lower the diversity is, the fewer the levels are divided. (3) A new update mechanism is proposed to reduce the number of selected features. In this mechanism, if the error rate of an offspring is higher than or is equal to that of its parent but selects more features, then the offspring is discarded and the parent is retained, otherwise, the offspring replaces its parent. Further, we discuss and analyze the contribution of these novelties to the diversity of population and the performance of classification. Finally, the results, compared with 8 state-of-the-art algorithms on 12 high-dimensional datasets, confirm the competitive performance of the proposed SLLABC on both classification accuracy and the size of the feature subset.

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“…Another example of hybridization of ACO and PSO is given in [27], related to PID parameter optimization on autonomous underwater vehicle control systems. Other approaches include the hybridization of ACO algorithms with differential evolution in [28,29] to solve TSP and cancer data classification problems. Additionally, the ACO metaheuristic proposed in [30] presents a different approach to diversify the solution space based on combining pairs of searching ants.…”

Section: Recent Trends In Aco Algorithmsmentioning

confidence: 99%

Rank-Based Ant System with Originality Reinforcement and Pheromone Smoothing

2022

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Ant Colony Optimization (ACO) encompasses a family of metaheuristics inspired by the foraging behaviour of ants. Since the introduction of the first ACO algorithm, called Ant System (AS), several ACO variants have been proposed in the literature. Owing to their superior performance over other alternatives, the most popular ACO algorithms are Rank-based Ant System (ASRank), Max-Min Ant System (MMAS) and Ant Colony System (ACS). While ASRank shows a fast convergence to high-quality solutions, its performance is improved by other more widely used ACO variants such as MMAS and ACS, which are currently considered the state-of-the-art ACO algorithms for static combinatorial optimization problems. With the purpose of diversifying the search process and avoiding early convergence to a local optimal, the proposed approach extends ASRank with an originality reinforcement strategy of the top-ranked solutions and a pheromone smoothing mechanism that is triggered before the algorithm reaches stagnation. The approach is tested on several symmetric and asymmetric Traveling Salesman Problem and Sequential Ordering Problem instances from TSPLIB benchmark. Our experimental results show that the proposed method achieves fast convergence to high-quality solutions and outperforms the current state-of-the-art ACO algorithms ASRank, MMAS and ACS, for most instances of the benchmark.

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Differential evolution and ACO based global optimal feature selection with fuzzy rough set for cancer data classification

Cited by 24 publications

References 36 publications

Multi-Criteria Decision Making Methods for Ensemble Feature Selection using q-Rung Orthopair Hesitant Fuzzy Distance and Similarity Measures

Multi-Criteria Decision Making Methods for Ensemble Feature Selection using q-Rung Orthopair Hesitant Fuzzy Distance and Similarity Measures

A self-adaptive level-based learning artificial bee colony algorithm for feature selection on high-dimensional classification

Rank-Based Ant System with Originality Reinforcement and Pheromone Smoothing

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