An improve feature selection algorithm for defect detection of glass bottles

Abstract Defect detection is an effective technology to guarantee the quality of the products. In this paper, the defect detection of glass bottles is taken as a classification problem. Features abstracted from the knocking sound signals of the glass bottles are used as the input of the classifier. Because the detection of bottles usually should be done in real time, feature selection which could reduce the dimensionality of data and simplify the learned model has become an important technology in this work. Based on our previous proposed algorithm named Shuffled Frog Leaping Algorithm-Improved Minimal Redundancy Maximal Relevance(SFLA-ImRMR), this paper focuses on improving the classification performance and proposes an improved feature selection algorithm. In the proposed algorithm, feature selection is combined with the training of the classifier, the wrapper approach is used to evaluate the selected features, Shuffled Frog Leaping Algorithm(SFLA) is used as the search algorithm, and BP Neural Network(BPNN) is used as the classifier. The proposed algorithm is named SFLA-ImRMR-BP and tested in the data sets built by the knocking sound signals of the glass bottles. Compared with SFLA-ImRMR and some other feature selection algorithms based on Evolutionary Computation(EC), features selected by SFLA-ImRMR-BP can achieve the highest classification performance.

[1]  Xin Zhao,et al.  A Fast Feature Selection Algorithm Based on Swarm Intelligence in Acoustic Defect Detection , 2018, IEEE Access.

[2]  Xue-wen Chen.  An improved branch and bound algorithm for feature selection , 2003, Pattern Recognit. Lett..

[3]  Xiaofeng Zhu,et al.  Graph self-representation method for unsupervised feature selection , 2017, Neurocomputing.

[4]  Zhigang Shang,et al.  Feature Selection based on manifold-learning with dynamic constraint handling differential evolution , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[5]  Xin Yao,et al.  A Survey on Evolutionary Computation Approaches to Feature Selection , 2016, IEEE Transactions on Evolutionary Computation.

[6]  K. De Jong,et al.  Effective Automated Feature Construction and Selection for Classification of Biological Sequences , 2014, PloS one.

[7]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[8]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Giovanni Felici,et al.  Integer programming models for feature selection: New extensions and a randomized solution algorithm , 2016, Eur. J. Oper. Res..

[10]  Vahid Chahkandi,et al.  Feature Selection with Chaotic Hybrid Artificial Bee Colony Algorithm based on Fuzzy (CHABCF) , 2013 .

[11]  Xing Liu,et al.  Particle swarm optimization-based feature selection in sentiment classification , 2016, Soft Comput..

[12]  Shunmugapriya Palanisamy.  Classifier Ensemble Design using Artificial Bee Colony based Feature Selection , 2012 .

[13]  Kevin E Lansey,et al.  Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm , 2003 .

[14]  Si-Yuan Jing,et al.  A hybrid genetic algorithm for feature subset selection in rough set theory , 2014, Soft Comput..

[15]  Mengjie Zhang,et al.  Improving Relevance Measures Using Genetic Programming , 2012, EuroGP.

[16]  Parham Moradi,et al.  An unsupervised feature selection algorithm based on ant colony optimization , 2014, Eng. Appl. Artif. Intell..

[17]  Chuen-Horng Lin,et al.  Study of image retrieval and classification based on adaptive features using genetic algorithm feature selection , 2014, Expert Syst. Appl..

[18]  Mengjie Zhang,et al.  Unsupervised Elimination of Redundant Features Using Genetic Programming , 2009, Australasian Conference on Artificial Intelligence.

[19]  Mengjie Zhang,et al.  Binary particle swarm optimisation for feature selection: A filter based approach , 2012, 2012 IEEE Congress on Evolutionary Computation.

[20]  Nurettin Acir.  Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm , 2005, Neural Computing & Applications.

[21]  Ali Broumandnia,et al.  Image steganalysis using improved particle swarm optimization based feature selection , 2018, Applied Intelligence.

[22]  Michael D. Todd,et al.  Automated Feature Design for Numeric Sequence Classification by Genetic Programming , 2015, IEEE Transactions on Evolutionary Computation.

[23]  Pa-Chun Wang,et al.  Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosis , 2011, Neural Computing and Applications.

[24]  Youwei Wang,et al.  Novel artificial bee colony based feature selection method for filtering redundant information , 2018, Applied Intelligence.