Proposed Feature Selection Approach Research Articles

PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection

The challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android. Therefore, it has attracted the attention of various researchers to develop an Android malware detection model using permission or a set of permissions. Academicians and researchers have used all extracted features in previous studies, resulting in overburdening while creating malware detection models. But, the effectiveness of the machine learning model depends on the relevant features, which help in reducing the value of misclassification errors and have excellent discriminative power. A feature selection framework is proposed in this research paper that helps in selecting the relevant features. In the first stage of the proposed framework, t-test, and univariate logistic regression are implemented on our collected feature data set to classify their capacity for detecting malware. Multivariate linear regression stepwise forward selection and correlation analysis are implemented in the second stage to evaluate the correctness of the features selected in the first stage. Furthermore, the resulting features are used as input in the development of malware detection models using three ensemble methods and a neural network with six different machine-learning algorithms. The developed models’ performance is compared using two performance parameters: F-measure and Accuracy. The experiment is performed by using half a million different Android apps. The empirical findings reveal that malware detection model developed using features selected by implementing proposed feature selection framework achieved higher detection rate as compared to the model developed using all extracted features data set. Further, when compared to previously developed frameworks or methodologies, the experimental results indicates that model developed in this study achieved an accuracy of 98.8%.

An Adapted Ant Colony Optimization for Feature Selection

ABSTRACT As information technologies evolve, they generate vast and ever-expanding datasets. This wealth of high-dimensional data presents challenges, including increased computational demands and difficulties in extracting valuable insights. The aim of feature selection is to address this complexity by reducing data dimensions with minimal information loss. Our proposed feature selection approach, the Feature Selection via Ant Colony Optimization algorithm, employs heuristic distance directly in its probability function, instead of using its inverse. The algorithm bypasses the need for sub-attribute sets, running multiple iterations to create a frequency order list from the collected routes, which informs feature importance. The efficacy of this technique has been validated through comparative experiments with other methods from scientific literature. To ensure fairness, these experiments used identical datasets, data partitioning strategies, classifiers, and performance metrics. Initially, the algorithm was compared with fifteen different algorithms, and subsequently benchmarked against three selected methods. The impact of feature selection on classification performance was statistically verified through comparisons before and after the feature selection process. Convergence performance of the proposed method has also been evaluated. Our findings robustly support the efficacy of the introduced approach in managing complex, multidimensional data effectively.

A Class Specific Feature Selection Method for Improving the Performance of Text Classification

Recently, a significant amount of research work has been carried out in the field of feature selection. Although these methods help to increase the accuracy of the machine learning classification, the selected subset of features considers all the classes and may not select recommendable features for a particular class. The main goal of our paper is to propose a new class-specific feature selection algorithm that is capable of selecting an appropriate subset of features for each class. In this regard, we first perform class binarization and then select the best features for each class. During the feature selection process, we deal with class imbalance problems and redundancy elimination. The Weighted Average Voting Ensemble method is used for the final classification. Finally, we carry out experiments to compare our proposed feature selection approach with the existing popular feature selection methods. The results prove that our feature selection method outperforms the existing methods with an accuracy of more than 37%.

Causality-Driven Efficient Feature Selection for Deep-Learning-Based Surface Roughness Prediction in Milling Machines

This paper studies the application of artificial intelligence to milling machines, focusing specifically on identifying the inputs (features) required for predicting surface roughness. Previous studies have extensively reviewed and presented useful features for surface roughness prediction. However, applying research findings to actual operational factories can be challenging due to the additional costs of sensor installations and the diverse environments present in each factory setting. To address these issues, in this paper, we introduced effective features for predicting surface roughness in situations where additional sensors are not installed in the existing environment. These features include feed per tooth, Fz; material removal rate, Q; and the load information. These features are suitable for use in highly constrained environments where separate sensor installation is not required, making it possible to apply the research findings in various factory environments. Additionally, to efficiently select the optimal subset for surface roughness prediction among subsets formed by available features, we apply causality to the feature selection method, proposing an approach called causality-driven efficient feature selection. The experimental results demonstrate that the features introduced in this paper are quite suitable for predicting surface roughness and that the proposed feature selection approach is more effective and efficient compared to existing selection methods.

MRI-Based Radiomics Analysis of Levator Ani Muscle for Predicting Urine Incontinence after Robot-Assisted Radical Prostatectomy.

The exact role of the levator ani (LA) muscle in male continence remains unclear, and so this study aims to shed light on the topic by characterizing MRI-derived radiomic features of LA muscle and their association with postoperative incontinence in men undergoing prostatectomy. In this retrospective study, 140 patients who underwent robot-assisted radical prostatectomy (RARP) for prostate cancer using preoperative MRI were identified. A biomarker discovery approach based on the optimal biomarker (OBM) method was used to extract features from MRI images, including morphological, intensity-based, and texture-based features of the LA muscle, along with clinical variables. Mathematical models were created using subsets of features and were evaluated based on their ability to predict continence outcomes. Univariate analysis showed that the best discriminators between continent and incontinent patients were patients age and features related to LA muscle texture. The proposed feature selection approach found that the best classifier used six features: age, LA muscle texture properties, and the ratio between LA size descriptors. This configuration produced a classification accuracy of 0.84 with a sensitivity of 0.90, specificity of 0.75, and an area under the ROC curve of 0.89. This study found that certain patient factors, such as increased age and specific texture properties of the LA muscle, can increase the odds of incontinence after RARP. The results showed that the proposed approach was highly effective and could distinguish and predict continents from incontinent patients with high accuracy.

Developing an Artificial Intelligence Based Model for Autism Spectrum Disorder Detection in Children

Sensory difficulties, such as an over or under responsiveness to noises, smells, or touch, are frequently present in individuals with Autism Spectrum Disease (ASD), a neurodevelopmental disorder. The condition's primary cause is hereditary, however early diagnosis and therapy can assist. Traditional clinical procedures may be expensive and time consuming, but in current history, deep learning based sophisticated diagnosis has emerged to supplement them. The goal of this study is to streamline the diagnostic procedure by identifying the most important characteristics and automating them using existing classification methods. We have looked at datasets including toddlers, kids, teens, and adults with autism spectrum disorder. To find the highest performing classification and feature set for these four ASD datasets, we compared state-of-the-art categorization and feature selection methods. Across datasets of toddlers, kids, teens, and adults, our experiments reveal that the multilayer perceptron (MLP) classifier achieves 100% accuracy with the fewest possible features. We also determine that the proposed feature selection approach ranks the most important characteristics the highest across all four ASD datasets.

Feature Selection as a Hedonic Coalition Formation Game for Arabic Topic Detection

Arabic topic detection is a Natural Language Processing (NLP) task that aims to assign a topic or a set of topics to a new document based on its content and selected features. Feature selection is an essential topic detection technique used to capture discriminative and descriptive features in documents to enhance the performance of topic detection. This paper proposes a novel feature selection approach formulated as a Hedonic Coalition Formation Game. We use the proposed method to describe each topic with a unique Topic Oriented Vocabulary (TOV) vector. We inspect the performances of the vocabularies obtained by solving the Hedonic game in topic detection over several Arabic corpora. In addition, the proposed feature selection method is compared with a Mutual Information (MI) based vocabulary vector generation method. The reported results show the effectiveness of the proposed feature selection approach by generating new vocabularies with highly discriminating features and reduced dimensions.

Evaluating the performance of ensemble classifiers in stock returns prediction using effective features

Stock market prediction is considered as an important yet challenging aspect of financial analysis. The difficulty of forecasting arises from volatile and non-linear nature of stock market, which is affected by varied uncertain factors, ranging from financial ratios to macroeconomic indicators. Recent advances in machine learning, particularly ensembles, have made it possible for academic researchers and financial practitioners to forecast the stock market more efficiently. The novelty of this work is to evaluate how stock return in an oil-dependent country (i.e., Iran), which has been facing stagflation for a long time due to economic and political issues, is affected by fundamental and macroeconomic indicators. Our main objectives are to (1) find the most important fundamental and macroeconomic indicators that control the stock returns of companies listed on the Tehran Stock Exchange (TSE); (2) compare the performance of newly developed bagging- and boosting-based ensembles in predicting annual real stock returns of the TSE; and (3) develop multiclass classification models to forecast stock returns. Prior studies mainly focused on developing binary classification models, which simply predict whether stock returns will be positive or negative in the future. We, however, design multiclass classification models to provide more information for the investors and reduce the uncertainties associated with the prediction. To this end, we first provide a comprehensive list of 57 potential features affecting the stock returns. Next, the data are carefully preprocessed and fed to 14 different bagging- and boosting-based ensembles (e.g., Random Forest, LightGBM, XGBoost, Extra-Trees, AdaBoost, CatBoost) to predict the stock returns. The performance of ensembles is evaluated through different measures (e.g., accuracy, F-score, G-mean). We then propose a novel feature selection method to identify the most contributing features to the stock returns. Our proposed model identifies nearly 65% of 57 original features as redundancy, resulting in 20 most significant features. The selected features are fed to the mentioned ensembles to re-predict the stock returns. Finally, the performance of stock returns forecasts with and without selected features is compared. To design the ensembles, we employ the data from listed companies on the TSE for a 15-year period, spanning between 2005 and 2020. Results suggest that boosting ensembles, in general, outperform bagging-based methods. Among the boosting ensembles, XGBoost and AdaBoost provide the best and worst predictive performance, respectively. Among the bagging-like ensembles, Rotation Forest is the most accurate one, whereas Random Patches performs the worst. Further, our proposed feature selection approach effectively identifies the most representative features for stock returns prediction and can be used as a reliable framework for future investment decisions.

Intelligent injury prediction for traumatic airway obstruction.

Airway obstruction is one of the crucial causes of death in trauma patients during the first aid. It is extremely challenging to accurately treat a great deal of casualties with airway obstruction in hospitals. The diagnosis of airway obstruction in an emergency mostly relies on the medical experience of physicians. In this paper, we propose the feature selection approach genetic algorithm-mean decrease impurity (GA-MDI) to effectively minimize the number of features as well as ensure the accuracy of prediction. Furthermore, we design a multi-modal neural network, called fully convolutional network with squeeze-and-excitation and multilayer perceptron (FCN-SE + MLP), to help physicians to predict the severity of airway obstruction. We validate the effectiveness of the proposed feature selection approach and multi-modal model on the emergency medical database from the Chinese General Hospital of the PLA. The experimental results show that GA-MDI outperforms the existing feature selection algorithms, while it is also validated that the model FCN-SE + MLP can effectively and accurately achieve the prediction of the severity of airway obstruction, which can assist clinicians in making treatment decisions for airway obstruction casualties.

Sequential feature selection for heart disease detection using random forest

Heart disease identification is one of the most challenging task that requires highly experienced cardiologists. However, in developing nations such as Ethiopia, there are a few cardiologists and heart disease detection is more challenging. As an alternative solution to cardiologist, this study proposed a more effective model for heart disease detection by employing random forest and sequential feature selection (SFS). SFS is an effective approach to improve the performance of random forest model on heart disease detection. SFS removes unrelated features in heart disease dataset that tends to mislead random forest model on heart disease detection. Thus, removing inappropriate and duplicate features from the training set with sequential feature selection approach plays significant role in improving the performance of the proposed model. The proposed feature selection approach is evaluated using real world clinical heart disease dataset collected from University of California Irvine (UCI) data repository. Empirical test on validation set reveals that the proposed model performs well as compared to the existing methods. Overall, the state of-the-art heart disease detection model with classification accuracy of 98.53% is proposed for heart disease detection using SFS and random forest model.

Optimized Signal Quality Assessment for Photoplethysmogram Signals Using Feature Selection.

With the increasing use of wearable healthcare devices for remote patient monitoring, reliable signal quality assessment (SQA) is required to ensure the high accuracy of interpretation and diagnosis on the recorded data from patients. Photoplethysmographic (PPG) signals non-invasively measured by wearable devices are extensively used to provide information about the cardiovascular system and its associated diseases. In this study, we propose an approach to optimize the quality assessment of the PPG signals. We used an ensemble-based feature selection scheme to enhance the prediction performance of the classification model to assess the quality of the PPG signals. Our approach for feature and subset size selection yielded the best-suited feature subset, which was optimized to differentiate between the clean and artifact corrupted PPG segments. A high discriminatory power was achieved between two classes on the test data by the proposed feature selection approach, which led to strong performance on all dependent and independent test datasets. We achieved accuracy, sensitivity, and specificity rates of higher than 0.93, 0.89, and 0.97, respectively, for dependent test datasets, independent of heartbeat type, i.e., atrial fibrillation (AF) or non-AF data including normal sinus rhythm (NSR), premature atrial contraction (PAC), and premature ventricular contraction (PVC). For independent test datasets, accuracy, sensitivity, and specificity rates were greater than 0.93, 0.89, and 0.97, respectively, on PPG data recorded from AF and non-AF subjects. These results were found to be more accurate than those of all of the contemporary methods cited in this work. As the results illustrate, the advantage of our proposed scheme is its robustness against dynamic variations in the PPG signal during long-term 14-day recordings accompanied with different types of physical activities and a diverse range of fluctuations and waveforms caused by different individual hemodynamic characteristics, and various types of recording devices. This robustness instills confidence in the application of the algorithm to various kinds of wearable devices as a reliable PPG signal quality assessment approach.

An Intelligent System for Parkinson's Diagnosis Using Hybrid Feature Selection Approach

Parkinson’s is the second most common neurodegenerative disorder after Alzheimer’s disease which adversely affects the nervous system of the patients. During the nascent stage, the symptoms of Parkinson’s disease are mild and sometimes go unnoticeable but as the disease progresses the symptoms go severe, so its diagnosis at an early stage is not easy. Recent research has shown that changes in speech or distortion in voice can be taken effectively used for early Parkinson’s detection. In this work, the authors propose a system of Parkinson's disease detection using speech signals. As the feature selection plays an important role during classification, authors have proposed a hybrid MIRFE feature selection approach. The result of the proposed feature selection approach is compared with the 5 standard feature selection methods by XGBoost classifier. The proposed MIRFE approach selects 40 features out of 754 features with a feature reduction ratio of 94.69%. An accuracy of 93.88% and area under curve (AUC) of 0.978 is obtained by the proposed system.

Empowering IoT Predictive Maintenance Solutions With AI: A Distributed System for Manufacturing Plant-Wide Monitoring

The emergence of Industry 4.0 and the rapid advances in the Industrial Internet of Things (IIoT) have provided manufacturers with the ability to remotely monitor the process by deploying automatic fault detection in an IoT-based predictive maintenance system. However, the monitoring targets are now manufacturing plant-wide instead of being just a local area. Multiple types of faults are involved and the conventional centralized cloud computing-based IoT solutions always lead to a heavy burden on the network bandwidth due to the large amount of sensor data collected frequently that has to be transmitted to the central server and this leads to poor response time for the monitoring system. To address this problem, this article develops an artificial intelligence-assisted distributed system for manufacturing plant-wide predictive maintenance applications. The developed distributed system relies on the feature selection technique to identity an optimal feature subset for each type of fault and is enabled by deploying each independent model built on the obtained feature subset into different edge nodes. The distributed approach enables the data to be processed near the sensors, requiring less data to be transmitted to the central cloud server reducing network delay and delivering more accurate results. In addition, our proposed feature selection approach is especially designed to accommodate the characteristics of IIoT data such as the lack of labels. The effectiveness of the proposed method is validated using the widely used public Tennessee Eastman dataset.

Classification of surface settlement levels induced by TBM driving in urban areas using random forest with data-driven feature selection

Prediction of surface settlements induced by urban area tunneling is challenging owing to the unique tunneling conditions of tunnel sites. This study presents a machine learning (ML) framework to predict the surface settlement level using a data-driven feature selection method. A large-scale database consisting of 42 settlement-influencing factors and 253 settlement measurements was acquired from a subway tunnel project in Hong Kong. The feature selection approach with three evaluation indices, i.e., predictive power score, mutual information, and feature importance, navigated the relevant features within the database. The random forest algorithm was adopted to predict the four classes of settlements defined according to their settlement levels. The efficiency of the proposed feature selection approach was verified by the accuracy and F1 score, which increased by 14.5% and 15.4%, respectively. The proposed framework can enhance the applicability of ML approaches for predicting surface settlement at complex TBM tunneling sites.

Analyzing the Features Affecting the Performance of Teachers during Covid-19: A Multilevel Feature Selection

COVID-19 is a profoundly contagious pandemic that has taken the world by storm and has afflicted different fields of life with negative effects. It has had a substantial impact on education which is evident from the transition from Face-to-Face (F2F) teaching to online mode of education and the rigid implementation of lockdown across the globe. This study examines the factors impacting the performance of teachers during the lockdown period of COVID-19 using various feature selection algorithms and Artificial Intelligence techniques. In this paper, we have proposed a novel multilevel feature selection for the prediction of the factors affecting the teachers’ satisfaction with online teaching and learning in COVID-19. The proposed multilevel feature selection is composed of the Fast Correlation Based Filter (FCBF), Mutual Information (MI), Relieff, and Particle Swarm Optimization (PSO) feature selection. The performance of the proposed feature selection approach is evaluated through the teachers’ benchmark dataset. We used a range of measures like accuracy, precision, f-measure, and recall to evaluate the performance of the proposed approach. We applied different machine learning approaches (SVM, LGBM, and ANN) with the proposed multilevel feature selection technique. The performance of the proposed approach is also compared with existing feature selection algorithms, and the results show the improvement in the performance of feature selection in terms of accuracy, precision, recall, and F-Measure. Proposed feature selection provides more than 80% accuracy with Light Weight Gradient Boosting Machine (LGBM).

A Cost-Efficient MFCC-Based Fault Detection and Isolation Technology for Electromagnetic Pumps

Fluid pumps serve critical purposes in hydraulic systems so their failure affects productivity, profitability, safety, etc. The need for proper condition monitoring and health assessment of these pumps cannot be overemphasized and this has resulted in extensive research studies on standard techniques for ensuring optimum fault detection and isolation (FDI) results for these pumps. Interestingly, mechanical vibrational signals reflect operating conditions and by exploring the robust time–frequency-domain feature extraction techniques, the underlying nonlinear characteristics can be captured for reliable fault diagnosis/condition assessment. This study is based on the use of vibrational signals for fault isolation of electromagnetic pumps. From the vibrational signals, Mel frequency cepstral coefficients (MFCCs), the first-order and the second-order differentials were extracted and the salient features selected by a rank-based recursive feature elimination (RFE) of uncorrelated features. The proposed framework was tested and validated on five VSC63A5 electromagnetic pumps at various fault conditions and isolated/classified using the Gaussian kernel SVM (SVM-RBF-RFE). Results show that the proposed feature selection approach is computationally cheaper and significantly improves diagnostics performance. In addition, the proposed framework yields a comparatively better diagnostics results on electromagnetic pumps in comparison with other diagnostics methods, hence a more reliable diagnostics tool for electromagnetic pumps.

BSSA: Binary Salp Swarm Algorithm With Hybrid Data Transformation for Feature Selection

Feature selection is a technique commonly used in Data Mining and Machine Learning. Traditional feature selection methods, when applied to large datasets, generate a large number of feature subsets. Selecting optimal features within this high dimensional data space is time-consuming and negatively affects the system's performance. This paper proposes a new binary Salp Swarm Algorithm (bSSA) for selecting the best feature set from transformed datasets. The proposed feature selection method first transforms the original data-set using Principal Component Analysis (PCA) and fast Independent Component Analysis (fastICA) based hybrid data transformation methods; next, a binary Salp Swarm optimizer is used for finding the best features. The proposed feature selection approach improves accuracy and eliminates the selection of irrelevant features. We validate our technique on fifteen different benchmark data sets. We conduct an extensive study to measure the performance and feature selection accuracy of the proposed technique. The proposed bSSA is compared to Binary Genetic Algorithm (bGA), Binary Binomial Cuckoo Search (bBCS), Binary Grey Wolf Optimizer (bGWO), Binary Competitive Swarm Optimizer (bCSO), and Binary Crow Search Algorithm (bCSA). The proposed method attains a mean accuracy of 95.26% with 7.78% features on PCA-fastICA transformed datasets. The results show that bSSA outperforms the existing methods for the majority of the performance measures.

Regularized Regression With Fuzzy Membership Embedding for Unsupervised Feature Selection

Although fuzziness universally diffuses in the real-world data, the fuzzy information is tricky to harness for feature selection such that it is rarely utilized. Therefore, how to efficiently exploit fuzzy information has become the major focus for feature selection recently. In this article, a novel unsupervised feature selection method is proposed via exploiting the sparse fuzzy membership efficiently. In general, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{2,1}$</tex-math></inline-formula> norm is utilized to induce sparsity, while Frobenius norm is used to prevent overfitting. To obtain sparsity and avoid overfitting simultaneously, adaptive loss regularization is introduced to the least-squares regression, such that a sparse and nontrivial projection matrix can be achieved via continuous interpolation between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{2,1}$</tex-math></inline-formula> and Frobenius regularization. Additionally, the fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means problem is further embedded with the adaptive loss regression model to avoid the trivial solution caused by the linearity of fuzzy membership. Therefore, the fuzzy cluster structure of fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means is exploited for the efficient feature selection. By performing fuzzy clustering and subspace regression simultaneously, the embedded problem is then reformulated into a general quadratic problem with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> ball constraint. Equipped with an auxiliary variable and standard augmented Lagrangian method, the quadratic problem, i.e., the corresponding dual problem can be solved with the closed form solutions regarding the fuzzy membership and the projection matrix. Consequently, empirical results are provided to demonstrate the effectiveness of the proposed feature selection approach.

Classification of multi-lingual tweets, into multi-class model using Naïve Bayes and semi-supervised learning

Twitter is a social media platform which has been proven to be a great tool for insights of emotions about products, policies etc. through a 280-character message called tweet, containing direct and unfiltered emotions by a large amount of user population. Twitter has attracted the attention of many researchers owing to the fact that every tweet is by default, public in nature which is not the case with Facebook. This paper proposes a model for multi-lingual (English and Roman Urdu) classification of tweets over diversely ranged classes (non-hierarchical architecture). Previous work in tweet classification is narrowly focused either on single language or either on uniform set of classes at most (Positive, Extremely Positive, Negative and Extremely Negative). The proposed model is based on semi-supervised learning and proposed feature selection approach makes it less dependent and highly adaptive for grabbing trending terms. This makes it a strong contender of choice for streaming data. In the methodology, using Naive Bayes learning algorithm for each phase, obtained remarkable accuracy of up to 87.16% leading from both KNN and SVM models which are popular for NLP and Text classification domains.

Developing comprehensive geocomputation tools for landslide susceptibility mapping: LSM tool pack

The primary aim of this research paper is to develop an easy-to-use tool package called Landslide Susceptibility Mapping Tool Pack (LSM Tool Pack) for producing landslide susceptibility maps based on integrating R with ArcMap Software. The proposed tool contains 5 main modules namely: (1) Data Preparation (DP), (2) Feature (Factor) Selection (FS), (3) Logistic Regression (LR), (4) Random Forest (RF) and (5) Performance Evaluation (PE). The FS module brings a novel approach to determine the best factor subset in the production of landslide susceptibility maps. The feature ranking values of factors were calculated by several feature ranging methods (i.e. chi-square, information gain, rank correlation, and random forest feature importance). The logistic regression method was used at the model prediction stage for each feature ranking and different models were produced for each ranking result. And, in the last step of the FS analysis, tests of statistical significance (i.e. Wilcoxon signed-rank test, F- Test, Kolmogorov Smirnov test, and One-Sample t-test) were used to determine the significance of the difference between models. As a result, the best factor sets determined by the FS module were used as input factors in the LR Module and the RF Module to produce LSMs. Also, users can calculate the performance metric of landslide susceptibility maps by several performance metrics (overall accuracy, Area under the ROC Curve (AUC) value, kappa, F1 score, and more) with additional integrated the PE Module in ArcMap Software. The LSM Tool Pack is applied to the Sinop province of the Black Sea region of Turkey. Considered the FS module, Case 1 was selected as the experimental dataset for this present study. In the selected Case 1, feature ranking method and statistically significant analysis were determined by Chi-square and F-Test, respectively. As a result, Model-12, which is contained 12 landslide causative factors, was determined as the optimum subset. According to the results obtained by the accuracy assessment process, the RF model showed the best prediction performance with an AUC value of 0.8898. On the other hand, the calculated AUC value was 0.8119 for the LR model. The experimental results (using with dataset in actual study area) confirm the ability of the proposed feature selection approach in the landslide susceptibility mapping process.