Part feature selection and family design in group technology using a fuzzy approach

Classification is the task of assigning objects to one of several predefined categories. However, developing a classification system is mostly hampered by the size of data. With the increase in the dimension of data, the chance of irrelevant, redundant, and noisy features or attributes also increases. Feature selection acts as a catalyst in reducing computation time and dimensionality, enhancing prediction performance or accuracy, and curtailing irrelevant or redundant data. The neuro-fuzzy approach is used for feature selection and classification with better insight by representing knowledge in symbolic forms. The neuro-fuzzy approach combines the merits of neural network and fuzzy logic to solve many complex machine learning problems. The objective of this article is to provide a generic introduction and a recent survey to neuro-fuzzy approaches for feature selection and classification in a wide area of machine learning problems. Some of the existing neuro-fuzzy models are also applied to standard datasets to demonstrate their applicability and performance.

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Wavelet Feature Selection Using Fuzzy Approach to Text Independent Speaker Recognition

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1093/ietfec/e88-a.3.779 ◽

2005 ◽

Vol E88-A (3) ◽

pp. 779-781 ◽

Cited By ~ 4

Author(s):

S.-Y. LUNG

Keyword(s):

Feature Selection ◽

Speaker Recognition ◽

Fuzzy Approach

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A Quantum Hybrid PSO Combined with Fuzzy K-NN Approach to Feature Selection and Cell Classification in Cervical Cancer Detection

10.20944/preprints201711.0193.v1 ◽

2017 ◽

Author(s):

Abdullah M. Iliyasu ◽

Chastine Fatichah

Keyword(s):

Cervical Cancer ◽

Feature Selection ◽

Cancer Detection ◽

Classification Accuracy ◽

Pso Algorithm ◽

Hybrid Technique ◽

Fuzzy Approach ◽

Hybrid Techniques ◽

Cervical Cells ◽

The Impact

A quantum hybrid (QH) intelligent approach that blends the adaptive search capability of the quantum-behaved particle swarm optimisation (QPSO) method with the intuitionistic rationality of traditional fuzzy k-nearest neighbours (Fuzzy k-NN) algorithm (known simply as the Q-Fuzzy approach) is proposed for efficient feature selection and classification of cells in cervical smeared (CS) images. From an initial multitude of seventeen (17) features describing the geometry, colour, and texture of the CS images, the QPSO stage of our proposed technique is used to select the best subset features (i.e. global best particles) that represent a pruned down collection of seven (7) features. Using a dataset of almost 1000 images, performance evaluation of our proposed Q-Fuzzy approach assesses the impact of our feature selection on classification accuracy by way of three experimental scenarios that are compared alongside two other approaches: The All-features (i.e. classification without prior feature selection) and another hybrid technique combining the standard PSO algorithm with the Fuzzy k-NN technique (P-Fuzzy approach). In the first and second scenarios, we further divided the assessment criteria in terms of classification accuracy based on the choice of best features and those in terms of the different categories of the cervical cells. In the third scenario, we introduced new QH hybrid techniques, i.e. QPSO combined with other supervised learning methods, and compared the classification accuracy alongside our proposed Q-Fuzzy approach. Furthermore, we employed statistical approaches to establish qualitative agreement with regards to the feature selection in scenarios 1 and 3. The synergy between the QPSO and Fuzzy k-NN in the proposed Q-Fuzzy approach marginally improves classification accuracy as manifest in the reduction in number cell features, which is crucial for effective cervical cancer detection and diagnosis.

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Review on Feature Selection and Classification using Neuro-Fuzzy Approaches

International Journal of Applied Evolutionary Computation ◽

10.4018/ijaec.2016100102 ◽

2016 ◽

Vol 7 (4) ◽

pp. 28-44 ◽

Cited By ~ 4

Author(s):

Saroj Biswas ◽

Monali Bordoloi ◽

Biswajit Purkayastha

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Computation Time ◽

Recent Survey ◽

Learning Problems ◽

Fuzzy Approach ◽

Redundant Data ◽

Classification Feature ◽

Research Article ◽

Neuro Fuzzy

This research article attempts to provide a recent survey on neuro-fuzzy approaches for feature selection and classification. Feature selection acts as a catalyst in reducing computation time and dimensionality, enhancing prediction performance or accuracy and curtailing irrelevant or redundant data. The neuro-fuzzy approach is used for feature selection and for providing some insight to the user about the symbolic knowledge embedded within the network. The neuro–fuzzy approach combines the merits of neural network and fuzzy logic to solve many complex machine learning problems. The objective of this article is to provide a generic introduction and a recent survey to neuro-fuzzy approaches for feature selection and classification in a wide area of machine learning problems. Some of the existing neuro-fuzzy models are also applied on standard datasets to demonstrate the applicability of neuro-fuzzy approaches.

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