Fisher Score Feature Selection Research Articles

A methodological integration of fisher score technique with intelligent machine learning methods for ball bearing fault investigation

This paper presents an efficient approach to detect, diagnose and estimate the severity of failures in various components of bearings in induction motors using vibration signature analysis. This automated method integrates the Fisher Score feature selection method and an efficient hyperparameter tuning model with machine learning models, including Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Decision Tree (DT), to accurately classify defects in bearings. This approach ensures accurate classification of bearing defects within less computation time. This work is carried out with vibration signals, recorded from a laboratory experimental setup on Machine Fault Simulator (MFS), focusing on ball bearing with defects in inner race, outer race and ball itself. Time and Frequency domain analysis are employed to compute the features for fault investigation in ball bearings using machine learning models. The computed results demonstrate that the proposed feature selection method with hyperparameter tuning achieved remarkable maximum accuracy among X, Y and XY combinations of datasets, with 97% in DT, 94% in SVM and 95.23% in k-NN models during the frequency domain analysis. Notably, these model accuracies improved to 99.04% in DT, 98% in SVM and 98% in k-NN during further analysis with Fisher Score technique. Consequently, the testing loss using this hyperparameter tuning function remains very low. Overall, this paper compares the results of time and frequency domain analysis and introduces a promising approach for both efficient and accurate fault detection and severity estimation in bearings of induction motors, potentially reducing the need for extensive manpower and sensor usage.

GBDT-IL: Incremental Learning of Gradient Boosting Decision Trees to Detect Botnets in Internet of Things.

The rapid development of the Internet of Things (IoT) has brought many conveniences to our daily life. However, it has also introduced various security risks that need to be addressed. The proliferation of IoT botnets is one of these risks. Most of researchers have had some success in IoT botnet detection using artificial intelligence (AI). However, they have not considered the impact of dynamic network data streams on the models in real-world environments. Over time, existing detection models struggle to cope with evolving botnets. To address this challenge, we propose an incremental learning approach based on Gradient Boosting Decision Trees (GBDT), called GBDT-IL, for detecting botnet traffic in IoT environments. It improves the robustness of the framework by adapting to dynamic IoT data using incremental learning. Additionally, it incorporates an enhanced Fisher Score feature selection algorithm, which enables the model to achieve a high accuracy even with a smaller set of optimal features, thereby reducing the system resources required for model training. To evaluate the effectiveness of our approach, we conducted experiments on the BoT-IoT, N-BaIoT, MedBIoT, and MQTTSet datasets. We compared our method with similar feature selection algorithms and existing concept drift detection algorithms. The experimental results demonstrated that our method achieved an average accuracy of 99.81% using only 25 features, outperforming similar feature selection algorithms. Furthermore, our method achieved an average accuracy of 96.88% in the presence of different types of drifting data, which is 2.98% higher than the best available concept drift detection algorithms, while maintaining a low average false positive rate of 3.02%.

An Integrated Approach for Diabetes Detection Using Fisher Score Feature Selection and Capsule Network

This paper investigates how the Fisher score feature selection approach can be used with capsule networks for diabetes detection. It also evaluates how well this algorithm works based on a number of evaluation parameters. The selected features using Fisher score method was then employed to train a capsule network model. Accuracy (94%), precision (94%0, recall (94%), F1 score (94%), and other performance evaluation metrics were thoroughly analyzed to determine the algorithm's efficacy. The results demonstrated that the combination of Fisher score feature selection and capsule networks yielded promising performance in diabetes detection. The selected features effectively captured the relevant information necessary for accurate classification The capsule network model was very accurate, which shows that it could be a good tool for diagnosing diabetes. Also, the accuracy and recall values showed that the algorithm could correctly place both positive and negative cases of diabetes, minimizing the risk of misdiagnosis. By merging the Fisher score feature selection approach with capsule networks, this research study contributes to advancing diabetes detection.

An Efficient Fingertip Photoplethysmographic Signal Artifact Detection Method: A Machine Learning Approach

A photoplethysmography method has recently been widely used to noninvasively measure blood volume changes during a cardiac cycle. Photoplethysmogram (PPG) signals are sensitive to artifacts that negatively impact the accuracy of many important measurements. In this paper, we propose an efficient system for detecting PPG signal artifacts acquired from a fingertip in the public healthcare database named Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) by using 11 features as the input of the random forest algorithm and classified the signals into two classes: acceptable and anomalous. A real-time algorithm is proposed to identify artifacts by using the method. The efficient Fisher score feature selection algorithm was used to order and select 11 relevant features from 19 available features that represented the PPG signal very effectively. Six machine learning algorithms (random forest, decision tree, Gaussian naïve Bayes, linear support vector machine, artificial neural network, and probabilistic neural network) were applied with the extracted features, and their classification accuracy was measured. Among them, the random forest had the best performance using only 11 out of 19 features with an accuracy of 85.68%. Our proposed method also achieved good sensitivity and specificity value of 86.57% and 85.09%, respectively. The proposed real-time algorithm can be an easy and convenient way for real-time PPG signal artifact detection using smartphones and wearable devices.

Assessment of Acoustic Features and Machine Learning for Parkinson's Detection.

This article presents a machine learning approach for Parkinson's disease detection. Potential multiple acoustic signal features of Parkinson's and control subjects are ascertained. A collaborated feature bank is created through correlated feature selection, Fisher score feature selection, and mutual information-based feature selection schemes. A detection model on top of the feature bank has been developed using the traditional Naïve Bayes, which proved state of the art. The Naïve Bayes detector on collaborative acoustic features can detect the presence of Parkinson's magnificently with a detection accuracy of 78.97% and precision of 0.926, under the hold-out cross validation. The collaborative feature bank on Naïve Bayes revealed distinguishable results as compared to many other recently proposed approaches. The simplicity of Naïve Bayes makes the system robust and effective throughout the detection process.

Fuzzy kernel k-medoids application with fisher score feature selection for predicting bank financial failure

The bank financial failure has a huge impact on the real sector, households and can even cause knock-on effects for other banks, therefore is important to predict bank financial failure. Prediction bank financial failure is like an early warning system for bank, because a bankruptcy doesn’t happen suddenly but there are indications that can be detected that is financial statement. Financial statement will be extracted to 6 components of CAMELS. The 2019, we had predicted bank financial failures using random forest with the data that used in Boyacioglu, Kara and Baykan paper (2009), however in this research we will make novelty by using fuzzy kernel k-medoids. Based on our results, fuzzy kernel k-medoids using RBF kernel with σ = 0.1 and 60% composition of training data has 100% for accuracy, sensitivity, precision, specificity, and f-score with 0.9 sec running time. If we compare to our previous research by random forest, fuzzy kernel k-medoids gives the highest accuracy prediction, but if we compare to Boyacioglu, Kara and Baykan research (2009), it’s has the same accuracy but with fuzzy kernel k-medoids, we can use only 60% of training data to learn.

Fractional Anisotropy changes in Parahippocampal Cingulum due to Alzheimer\u2019s Disease

Current treatments for Alzheimer’s disease are only symptomatic and limited to reduce the progression rate of the mental deterioration. Mild Cognitive Impairment, a transitional stage in which the patient is not cognitively normal but do not meet the criteria for specific dementia, is associated with high risk for development of Alzheimer’s disease. Thus, non-invasive techniques to predict the individual’s risk to develop Alzheimer’s disease can be very helpful, considering the possibility of early treatment. Diffusion Tensor Imaging, as an indicator of cerebral white matter integrity, may detect and track earlier evidence of white matter abnormalities in patients developing Alzheimer’s disease. Here we performed a voxel-based analysis of fractional anisotropy in three classes of subjects: Alzheimer’s disease patients, Mild Cognitive Impairment patients, and healthy controls. We performed Support Vector Machine classification between the three groups, using Fisher Score feature selection and Leave-one-out cross-validation. Bilateral intersection of hippocampal cingulum and parahippocampal gyrus (referred as parahippocampal cingulum) is the region that best discriminates Alzheimer’s disease fractional anisotropy values, resulting in an accuracy of 93% for discriminating between Alzheimer’s disease and controls, and 90% between Alzheimer’s disease and Mild Cognitive Impairment. These results suggest that pattern classification of Diffusion Tensor Imaging can help diagnosis of Alzheimer’s disease, specially when focusing on the parahippocampal cingulum.

Event Identification by F-ELM Model for $\varphi$ -OTDR Fiber-Optic Distributed Disturbance Sensor

A novel event identification method, which combines the extreme learning machine (ELM) and fisher score feature selection method, is proposed to reduce the nuisance alarm rate (NAR) in fiber-optic distributed disturbance sensors based on phase-sensitive optical time-domain reflectometer (φ-OTDR). Through constructing 25.05 km long φ-OTDR experimental system and analyzing the selected features with the ELM, four kinds of real disturbance events, including watering, climbing, knocking and pressing, and a false disturbance event can be effectively identified. Experimental results show that the average identification rate of five disturbance events exceeds 95%, the identification time is below 0.1 s and the NAR is 4.67% through selecting 25 features. Compared with the ELM model without feature selection, the ELM model with feature selection by fisher method has several distinguished advantages of higher identification rate, shorter identification time, and lower NAR.

Indonesia Composite Index Prediction using Fuzzy Support Vector Regression with Fisher Score Feature Selection

A precise forecast of stock price indexes may return a profit for investors. According to CNN Money, in the same month, as much as 93% of global investors have lost money for trading stock. One of the stock price indexes is the stock composite index. Exact predictions of the stock composite index can be critical for creating powerful market exchanging strategies. In this paper, a modified supervised learning method used to solve regression problems, Fuzzy Support Vector Regression (FSVR) is focused. As the complexity of many factors influences the movement of stock price prediction, the prediction results of Support Vector Regression (SVR) cannot always meet people with precision. Thus, this study implies Fuzzy Support Vector Regression (FSVR) stock prediction model, in which fuzzy membership with mapping function is employed to generate a precise price fluctuation of stock. To assure the use of features on model prediction, Fisher Score is used to find high-quality features that can enhance the accuracy. Indonesia Composite Index or Jakarta Composite Index (JKSE) is considered as input data and the result showed that Fisher Score could be applied as feature selection on Indonesia Composite Index prediction with the best model is eleven out of fifteen features with 80% of training data with 0.043529error.

Support Vector Machine with Fisher Score Feature Selection to Predict Disease-Resistant Gene in Rice

Indonesia is one of the leading rice producers and biggest rice consumers in the world. Hence, the sustainability of rice production systems needs to be considered. Bacterial leaf blight is one of the diseases that distract the growth of rice. But, BLB can be controlled through the development of BLB resistant. The experimental method (in vitro) is one of common ways to prevent it, but this method is not efficient and having a big error. Thus, a new method called computational method required to overcome these shortcomings. There are three steps of that method in predicting genes resistance disease which are extraction, feature selection, and machine learning. The global encoding was used in the feature extraction process, then fisher score was used to select the feature. After that, a model that represents the data to analyze disease-resistant gene in rice was built. The Support Vector Machine (SVM) was applied. The result was obtained that with only ten features and the performance of purpose method, the model could represent protein information 90.91% with training data used 90%. It indicated that it was very useful. It could predict disease-resistant gene in rice with high accuracy short running time and low dimension data.

Subhealth state classification with AdaBoost learner

Biopsychosocial approaches are the mainstay diagnostic methods for subhealth. This paper introduces the AdaBoost Learner to handle this issue. AdaBoost algorithm combine a series of weak classifiers, each of which performs slightly better than random guessing, to a strong one. In this paper, the AdaBoost Learners with discriminant classifiers and decision trees are built and two strong classifiers, support vector machine (SVM) and k–nearest neighbour (kNN), are adopted as control experiments. Two classification processes are constructed to distinguish health states and subhealth types respectively, where Fisher Score feature selection is for comparing performance with different feature subsets. Results indicate that the AdaBoost Learner with decision trees is the best among four classifiers in health states classification while the one with discriminant classifiers has the greatest performance in subhealth types classification. In health states classification, the highest accuracy reached 85.76% with 320 questions and 87.58% with 120 questions in subhealth types classification.