scholarly journals Binary Competitive Swarm Optimizer Approaches for Feature Selection

Computation ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 31 ◽  
Author(s):  
Jingwei Too ◽  
Abdul Rahim Abdullah ◽  
Norhashimah Mohd Saad
Keyword(s):  
Feature Selection ◽  
Transfer Functions ◽  
Computational Cost ◽  
Combinatorial Problem ◽  
Search Space ◽  
Relevant Information ◽  
Classification Performance ◽  
Feature Size ◽  
Benchmark Datasets ◽  
Low Computational Cost

Feature selection is known as an NP-hard combinatorial problem in which the possible feature subsets increase exponentially with the number of features. Due to the increment of the feature size, the exhaustive search has become impractical. In addition, a feature set normally includes irrelevant, redundant, and relevant information. Therefore, in this paper, binary variants of a competitive swarm optimizer are proposed for wrapper feature selection. The proposed approaches are used to select a subset of significant features for classification purposes. The binary version introduced here is performed by employing the S-shaped and V-shaped transfer functions, which allows the search agents to move on the binary search space. The proposed approaches are tested by using 15 benchmark datasets collected from the UCI machine learning repository, and the results are compared with other conventional feature selection methods. Our results prove the capability of the proposed binary version of the competitive swarm optimizer not only in terms of high classification performance, but also low computational cost.

scholarly journals Variable-Length Particle Swarm Optimization for Feature Selection on High-Dimensional Classification

2021 ◽  
Author(s):  
B Tran ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Computational Cost ◽  
Particle Swarm ◽  
Search Space ◽  
Classification Performance ◽  
Variable Length ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Local Optima

© 1997-2012 IEEE. With a global search mechanism, particle swarm optimization (PSO) has shown promise in feature selection (FS). However, most of the current PSO-based FS methods use a fix-length representation, which is inflexible and limits the performance of PSO for FS. When applying these methods to high-dimensional data, it not only consumes a significant amount of memory but also requires a high computational cost. Overcoming this limitation enables PSO to work on data with much higher dimensionality which has become more and more popular with the advance of data collection technologies. In this paper, we propose the first variable-length PSO representation for FS, enabling particles to have different and shorter lengths, which defines smaller search space and therefore, improves the performance of PSO. By rearranging features in a descending order of their relevance, we facilitate particles with shorter lengths to achieve better classification performance. Furthermore, using the proposed length changing mechanism, PSO can jump out of local optima, further narrow the search space and focus its search on smaller and more fruitful area. These strategies enable PSO to reach better solutions in a shorter time. Results on ten high-dimensional datasets with varying difficulties show that the proposed variable-length PSO can achieve much smaller feature subsets with significantly higher classification performance in much shorter time than the fixed-length PSO methods. The proposed method also outperformed the compared non-PSO FS methods in most cases. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

scholarly journals Particle swarm optimisation for feature selection: A hybrid filter-wrapper approach

2021 ◽  
Author(s):  
T Butler-Yeoman ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Computational Cost ◽  
Particle Swarm ◽  
Classification Performance ◽  
Particle Swarm Optimisation ◽  
Computational Time ◽  
Advantages And Disadvantages ◽  
Benchmark Datasets ◽  
Selection Algorithms

© 2015 IEEE. Feature selection is an important pre-processing step, which can reduce the dimensionality of a dataset and increase the accuracy and efficiency of a learning/classification algorithm. However, existing feature selection algorithms mainly wrappers and filters have their own advantages and disadvantages. This paper proposes two filter-wrapper hybrid feature selection algorithms based on particle swarm optimisation (PSO), where the first algorithm named FastPSO combined filter and wrapper into the search process of PSO for feature selection with most of the evaluations as filters and a small number of evaluations as wrappers. The second algorithm named RapidPSO further reduced the number of wrapper evaluations. Theoretical analysis on FastPSO and RapidPSO is conducted to investigate their complexity. FastPSO and RapidPSO are compared with a pure wrapper algorithm named WrapperPSO and a pure filter algorithm named FilterPSO on nine benchmark datasets of varying difficulty. The experimental results show that both FastPSO and RapidPSO can successfully reduce the number of features and simultaneously increase the classification performance over using all features. The two proposed algorithms maintain the high classification performance achieved by WrapperPSO and significantly reduce the computational time, although the number of features is larger. At the same time, they increase the classification accuracy of FilterPSO and reduce the number of features, but increased the computational cost. FastPSO outperformed RapidPSO in terms of the classification accuracy and the number of features, but increased the computational time, which shows the trade-off between the efficiency and effectiveness. © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

scholarly journals Particle swarm optimisation for feature selection: A hybrid filter-wrapper approach

2021 ◽  
Author(s):  
T Butler-Yeoman ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Computational Cost ◽  
Particle Swarm ◽  
Classification Performance ◽  
Particle Swarm Optimisation ◽  
Computational Time ◽  
Advantages And Disadvantages ◽  
Benchmark Datasets ◽  
Selection Algorithms

© 2015 IEEE. Feature selection is an important pre-processing step, which can reduce the dimensionality of a dataset and increase the accuracy and efficiency of a learning/classification algorithm. However, existing feature selection algorithms mainly wrappers and filters have their own advantages and disadvantages. This paper proposes two filter-wrapper hybrid feature selection algorithms based on particle swarm optimisation (PSO), where the first algorithm named FastPSO combined filter and wrapper into the search process of PSO for feature selection with most of the evaluations as filters and a small number of evaluations as wrappers. The second algorithm named RapidPSO further reduced the number of wrapper evaluations. Theoretical analysis on FastPSO and RapidPSO is conducted to investigate their complexity. FastPSO and RapidPSO are compared with a pure wrapper algorithm named WrapperPSO and a pure filter algorithm named FilterPSO on nine benchmark datasets of varying difficulty. The experimental results show that both FastPSO and RapidPSO can successfully reduce the number of features and simultaneously increase the classification performance over using all features. The two proposed algorithms maintain the high classification performance achieved by WrapperPSO and significantly reduce the computational time, although the number of features is larger. At the same time, they increase the classification accuracy of FilterPSO and reduce the number of features, but increased the computational cost. FastPSO outperformed RapidPSO in terms of the classification accuracy and the number of features, but increased the computational time, which shows the trade-off between the efficiency and effectiveness. © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

scholarly journals Variable-Length Particle Swarm Optimization for Feature Selection on High-Dimensional Classification

2021 ◽  
Author(s):  
B Tran ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Computational Cost ◽  
Particle Swarm ◽  
Search Space ◽  
Classification Performance ◽  
Variable Length ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Local Optima

© 1997-2012 IEEE. With a global search mechanism, particle swarm optimization (PSO) has shown promise in feature selection (FS). However, most of the current PSO-based FS methods use a fix-length representation, which is inflexible and limits the performance of PSO for FS. When applying these methods to high-dimensional data, it not only consumes a significant amount of memory but also requires a high computational cost. Overcoming this limitation enables PSO to work on data with much higher dimensionality which has become more and more popular with the advance of data collection technologies. In this paper, we propose the first variable-length PSO representation for FS, enabling particles to have different and shorter lengths, which defines smaller search space and therefore, improves the performance of PSO. By rearranging features in a descending order of their relevance, we facilitate particles with shorter lengths to achieve better classification performance. Furthermore, using the proposed length changing mechanism, PSO can jump out of local optima, further narrow the search space and focus its search on smaller and more fruitful area. These strategies enable PSO to reach better solutions in a shorter time. Results on ten high-dimensional datasets with varying difficulties show that the proposed variable-length PSO can achieve much smaller feature subsets with significantly higher classification performance in much shorter time than the fixed-length PSO methods. The proposed method also outperformed the compared non-PSO FS methods in most cases. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

scholarly journals Feature Selection using Genetic Programming

2019 ◽  
Vol 3 (2) ◽  
pp. 11-18
Author(s):  
George Mweshi
Keyword(s):  
Feature Selection ◽  
Genetic Programming ◽  
Information Gain ◽  
Principal Component ◽  
Search Space ◽  
Classification Performance ◽  
The Other ◽  
Searching Strategy ◽  
Mining Algorithms ◽  
Feature Selection Techniques

Extracting useful and novel information from the large amount of collected data has become a necessity for corporations wishing to maintain a competitive advantage. One of the biggest issues in handling these significantly large datasets is the curse of dimensionality. As the dimension of the data increases, the performance of the data mining algorithms employed to mine the data deteriorates. This deterioration is mainly caused by the large search space created as a result of having irrelevant, noisy and redundant features in the data. Feature selection is one of the various techniques that can be used to remove these unnecessary features. Feature selection consequently reduces the dimension of the data as well as the search space which in turn increases the efficiency and the accuracy of the mining algorithms. In this paper, we investigate the ability of Genetic Programming (GP), an evolutionary algorithm searching strategy capable of automatically finding solutions in complex and large search spaces, to perform feature selection. We implement a basic GP algorithm and perform feature selection on 5 benchmark classification datasets from UCI repository. To test the competitiveness and feasibility of the GP approach, we examine the classification performance of four classifiers namely J48, Naives Bayes, PART, and Random Forests using the GP selected features, all the original features and the features selected by the other commonly used feature selection techniques i.e. principal component analysis, information gain, relief-f and cfs. The experimental results show that not only does GP select a smaller set of features from the original features, classifiers using GP selected features achieve a better classification performance than using all the original features. Furthermore, compared to the other well-known feature selection techniques, GP achieves very competitive results.

Fuzzy Mutual Information Feature Selection Based on Representative Samples

2018 ◽  
Vol 6 (1) ◽  
pp. 58-72
Author(s):  
Omar A. M. Salem ◽  
Liwei Wang
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Classification Performance ◽  
Feature Subset ◽  
Classification Models ◽  
Negative Effect ◽  
Benchmark Datasets ◽  
Real World Datasets ◽  
And Storage ◽  
Representative Samples

Building classification models from real-world datasets became a difficult task, especially in datasets with high dimensional features. Unfortunately, these datasets may include irrelevant or redundant features which have a negative effect on the classification performance. Selecting the significant features and eliminating undesirable features can improve the classification models. Fuzzy mutual information is widely used feature selection to find the best feature subset before classification process. However, it requires more computation and storage space. To overcome these limitations, this paper proposes an improved fuzzy mutual information feature selection based on representative samples. Based on benchmark datasets, the experiments show that the proposed method achieved better results in the terms of classification accuracy, selected feature subset size, storage, and stability.

scholarly journals A New Representation in PSO for Discretization-Based Feature Selection

2021 ◽  
Author(s):  
B Tran ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Search Space ◽  
Classification Performance ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Learning Capacity ◽  
Feature Interactions

In machine learning, discretization and feature selection (FS) are important techniques for preprocessing data to improve the performance of an algorithm on high-dimensional data. Since many FS methods require discrete data, a common practice is to apply discretization before FS. In addition, for the sake of efficiency, features are usually discretized individually (or univariate). This scheme works based on the assumption that each feature independently influences the task, which may not hold in cases where feature interactions exist. Therefore, univariate discretization may degrade the performance of the FS stage since information showing feature interactions may be lost during the discretization process. Initial results of our previous proposed method [evolve particle swarm optimization (EPSO)] showed that combining discretization and FS in a single stage using bare-bones particle swarm optimization (BBPSO) can lead to a better performance than applying them in two separate stages. In this paper, we propose a new method called potential particle swarm optimization (PPSO) which employs a new representation that can reduce the search space of the problem and a new fitness function to better evaluate candidate solutions to guide the search. The results on ten high-dimensional datasets show that PPSO select less than 5% of the number of features for all datasets. Compared with the two-stage approach which uses BBPSO for FS on the discretized data, PPSO achieves significantly higher accuracy on seven datasets. In addition, PPSO obtains better (or similar) classification performance than EPSO on eight datasets with a smaller number of selected features on six datasets. Furthermore, PPSO also outperforms the three compared (traditional) methods and performs similar to one method on most datasets in terms of both generalization ability and learning capacity. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Nested Kriging Surrogates for Rapid Multi-Objective Optimization of Compact Microwave Components

2021 ◽  
Vol 35 (11) ◽  
pp. 1344-1345
Author(s):  
Anna Pietrenko-Dabrowska ◽  
Slawomir Koziel
Keyword(s):  
Computational Cost ◽  
Search Space ◽  
Optimal Designs ◽  
Minimum Size ◽  
Space Region ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Microwave Components ◽  
Low Computational Cost

A procedure for rapid EM-based multi-objective optimization of compact microwave components is presented. Our methodology employs a recently developed nested kriging modelling to identify the search space region containing the Pareto-optimal designs, and to construct a fast surrogate model. The latter permits determination of the initial Pareto set, further refined using a separate surrogate-assisted process. As an illustration, a three-section impedance transformer is designed for the best matching and minimum size. The set of trade-off designs is produced at the low computational cost of only a few hundred of high-fidelity EM simulations of the transformer circuit despite a large number of its geometry parameters.

Hybrid pooling with wavelets for convolutional neural networks

2022 ◽  
pp. 1-10
Author(s):  
Daniel Trevino-Sanchez ◽  
Vicente Alarcon-Aquino
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Computational Cost ◽  
Relevant Information ◽  
Accuracy Improvement ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Augmentation Techniques ◽  
High Computational Cost

The need to detect and classify objects correctly is a constant challenge, being able to recognize them at different scales and scenarios, sometimes cropped or badly lit is not an easy task. Convolutional neural networks (CNN) have become a widely applied technique since they are completely trainable and suitable to extract features. However, the growing number of convolutional neural networks applications constantly pushes their accuracy improvement. Initially, those improvements involved the use of large datasets, augmentation techniques, and complex algorithms. These methods may have a high computational cost. Nevertheless, feature extraction is known to be the heart of the problem. As a result, other approaches combine different technologies to extract better features to improve the accuracy without the need of more powerful hardware resources. In this paper, we propose a hybrid pooling method that incorporates multiresolution analysis within the CNN layers to reduce the feature map size without losing details. To prevent relevant information from losing during the downsampling process an existing pooling method is combined with wavelet transform technique, keeping those details "alive" and enriching other stages of the CNN. Achieving better quality characteristics improves CNN accuracy. To validate this study, ten pooling methods, including the proposed model, are tested using four benchmark datasets. The results are compared with four of the evaluated methods, which are also considered as the state-of-the-art.

MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS FOR FILTER BASED FEATURE SELECTION IN CLASSIFICATION

2013 ◽  
Vol 22 (04) ◽  
pp. 1350024 ◽  
Author(s):  
BING XUE ◽  
LIAM CERVANTE ◽  
LIN SHANG ◽  
WILL N. BROWNE ◽  
MENGJIE ZHANG
Keyword(s):  
Feature Selection ◽  
Evaluation Criteria ◽  
Classification Performance ◽  
Classification Problems ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Benchmark Datasets ◽  
Selection Algorithms ◽  
Filter Algorithms ◽  
Single Objective

Feature selection is a multi-objective problem with the two main conflicting objectives of minimising the number of features and maximising the classification performance. However, most existing feature selection algorithms are single objective and do not appropriately reflect the actual need. There are a small number of multi-objective feature selection algorithms, which are wrapper based and accordingly are computationally expensive and less general than filter algorithms. Evolutionary computation techniques are particularly suitable for multi-objective optimisation because they use a population of candidate solutions and are able to find multiple non-dominated solutions in a single run. However, the two well-known evolutionary multi-objective algorithms, non-dominated sorting based multi-objective genetic algorithm II (NSGAII) and strength Pareto evolutionary algorithm 2 (SPEA2) have not been applied to filter based feature selection. In this work, based on NSGAII and SPEA2, we develop two multi-objective, filter based feature selection frameworks. Four multi-objective feature selection methods are then developed by applying mutual information and entropy as two different filter evaluation criteria in each of the two proposed frameworks. The proposed multi-objective algorithms are examined and compared with a single objective method and three traditional methods (two filters and one wrapper) on eight benchmark datasets. A decision tree is employed to test the classification performance. Experimental results show that the proposed multi-objective algorithms can automatically evolve a set of non-dominated solutions that include a smaller number of features and achieve better classification performance than using all features. NSGAII and SPEA2 outperform the single objective algorithm, the two traditional filter algorithms and even the traditional wrapper algorithm in terms of both the number of features and the classification performance in most cases. NSGAII achieves similar performance to SPEA2 for the datasets that consist of a small number of features and slightly better results when the number of features is large. This work represents the first study on NSGAII and SPEA2 for filter feature selection in classification problems with both providing field leading classification performance.

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