Metaheuristic Based IDS Using Multi-objective Wrapper Feature Selection and Neural Network Classification

The goal of the current study is to develop a diagnosis model for chili pepper disease diagnosis by applying filter and wrapper feature selection methods as well as a Multi-Layer Perceptron Neural Network (MLPNN). The data used for developing the model include 1) types, 2) causative agents, 3) areas of infection, 4) growth stages of infection, 5) conditions, 6) symptoms, and 7) 14 types of chili pepper diseases. These datasets were applied to the 3 feature selection techniques, including information gain, gain ratio, and wrapper. After selecting the key features, the selected datasets were utilized to develop the diagnosis model towards the application of MLPNN. According to the model’s effectiveness evaluation results, estimated by 10-fold cross-validation, it can be seen that the diagnosis model developed by applying the wrapper method along with MLPNN provided the highest level of effectiveness, with an accuracy of 98.91%, precision of 98.92%, and recall of 98.89%. The findings showed that the developed model is applicable.

Download Full-text

A new wrapper feature selection approach using neural network

Neurocomputing ◽

10.1016/j.neucom.2010.04.003 ◽

2010 ◽

Vol 73 (16-18) ◽

pp. 3273-3283 ◽

Cited By ~ 95

Author(s):

Md. Monirul Kabir ◽

Md. Monirul Islam ◽

Kazuyuki Murase

Keyword(s):

Neural Network ◽

Feature Selection ◽

Selection Approach ◽

Feature Selection Approach ◽

Wrapper Feature Selection

Download Full-text

A novel automatic diagnostic approach based on nystagmus feature selection and neural network classification

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society ◽

10.1109/iecon.2016.7793010 ◽

2016 ◽

Cited By ~ 3

Author(s):

Amine Ben Slama ◽

Aymen Mouelhi ◽

Hanene Sahli ◽

Sondes Manoubi ◽

Mamia Ben Salah ◽

...

Keyword(s):

Neural Network ◽

Feature Selection ◽

Diagnostic Approach ◽

Neural Network Classification

Download Full-text

CFSES Optimization Feature Selection with Neural Network Classification for Microarray Data Analysis

2018 2nd International Conference on Data Science and Business Analytics (ICDSBA) ◽

10.1109/icdsba.2018.00016 ◽

2018 ◽

Author(s):

Bichitrananda Patra ◽

Sudhansu Sekhar Bisoyi

Keyword(s):

Neural Network ◽

Feature Selection ◽

Data Analysis ◽

Microarray Data ◽

Microarray Data Analysis ◽

Neural Network Classification

Download Full-text

Hybrid Multi-objective Optimization Approach for Neural Network Classification Using Local Search

Advances in Intelligent Systems and Computing - Innovations in Computer Science and Engineering ◽

10.1007/978-981-10-0419-3_21 ◽

2016 ◽

pp. 171-179 ◽

Cited By ~ 2

Author(s):

Seema Mane ◽

Shilpa Sonawani ◽

Sachin Sakhare

Keyword(s):

Neural Network ◽

Local Search ◽

Optimization Approach ◽

Multi Objective Optimization ◽

Multi Objective ◽

Neural Network Classification

Download Full-text

Deep Neural Network Feature Selection Approaches for Data-Driven Prognostic Model of Aircraft Engines

Aerospace ◽

10.3390/aerospace7090132 ◽

2020 ◽

Vol 7 (9) ◽

pp. 132

Author(s):

Phattara Khumprom ◽

David Grewell ◽

Nita Yodo

Keyword(s):

Neural Network ◽

Feature Selection ◽

Gas Turbine ◽

Deep Neural Network ◽

Data Driven ◽

Turbine Engines ◽

Gas Turbine Engines ◽

Aircraft Engines ◽

Selection Methods ◽

Wrapper Feature Selection

Predicting Remaining Useful Life (RUL) of systems has played an important role in various fields of reliability engineering analysis, including in aircraft engines. RUL prediction is critically an important part of Prognostics and Health Management (PHM), which is the reliability science that is aimed at increasing the reliability of the system and, in turn, reducing the maintenance cost. The majority of the PHM models proposed during the past few years have shown a significant increase in the amount of data-driven deployments. While more complex data-driven models are often associated with higher accuracy, there is a corresponding need to reduce model complexity. One possible way to reduce the complexity of the model is to use the features (attributes or variables) selection and dimensionality reduction methods prior to the model training process. In this work, the effectiveness of multiple filter and wrapper feature selection methods (correlation analysis, relief forward/backward selection, and others), along with Principal Component Analysis (PCA) as a dimensionality reduction method, was investigated. A basis algorithm of deep learning, Feedforward Artificial Neural Network (FFNN), was used as a benchmark modeling algorithm. All those approaches can also be applied to the prognostics of an aircraft gas turbine engines. In this paper, the aircraft gas turbine engines data from NASA Ames prognostics data repository was used to test the effectiveness of the filter and wrapper feature selection methods not only for the vanilla FFNN model but also for Deep Neural Network (DNN) model. The findings show that applying feature selection methods helps to improve overall model accuracy and significantly reduced the complexity of the models.

Download Full-text