Minimal cross-correlation criterion for speech emotion multi-level feature selection

2015 ◽  
Author(s):  
Tatjana Liogiene ◽  
Gintautas Tamulevicius
Keyword(s):  
Feature Selection ◽  
Cross Correlation ◽  
Correlation Criterion ◽  
Multi Level

scholarly journals A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection

2022 ◽  
Vol 12 (1) ◽  
pp. 55
Author(s):  
Fatih Demir ◽  
Kamran Siddique ◽  
Mohammed Alswaitti ◽  
Kursat Demir ◽  
Abdulkadir Sengur
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Early Diagnosis ◽  
Neurodegenerative Disorder ◽  
Bayesian Optimization ◽  
L1 Norm ◽  
Bayesian Optimization Algorithm ◽  
Knn Classifier ◽  
Multi Level

Parkinson’s disease (PD), which is a slowly progressing neurodegenerative disorder, negatively affects people’s daily lives. Early diagnosis is of great importance to minimize the effects of PD. One of the most important symptoms in the early diagnosis of PD disease is the monotony and distortion of speech. Artificial intelligence-based approaches can help specialists and physicians to automatically detect these disorders. In this study, a new and powerful approach based on multi-level feature selection was proposed to detect PD from features containing voice recordings of already-diagnosed cases. At the first level, feature selection was performed with the Chi-square and L1-Norm SVM algorithms (CLS). Then, the features that were extracted from these algorithms were combined to increase the representation power of the samples. At the last level, those samples that were highly distinctive from the combined feature set were selected with feature importance weights using the ReliefF algorithm. In the classification stage, popular classifiers such as KNN, SVM, and DT were used for machine learning, and the best performance was achieved with the KNN classifier. Moreover, the hyperparameters of the KNN classifier were selected with the Bayesian optimization algorithm, and the performance of the proposed approach was further improved. The proposed approach was evaluated using a 10-fold cross-validation technique on a dataset containing PD and normal classes, and a classification accuracy of 95.4% was achieved.

DeepEnzyPred: A Bi-Layered Deep Learning Framework for prediction of Bacteriophage Enzymes and their Sub-Hydrolases Enzymes via Novel Multi Level- Multi Thresholds Feature Selection technique

2020 ◽  
Author(s):  
Yu Wang ◽  
ZAHEER ULLAH KHAN ◽  
Shaukat Ali ◽  
Maqsood Hayat
Keyword(s):  
Feature Selection ◽  
Bacterial Infections ◽  
Large Scale ◽  
Computational Models ◽  
Genetic Material ◽  
Feature Space ◽  
Classification Model ◽  
Antibacterial Drug ◽  
Accurate Identification ◽  
Multi Level

Abstract BackgroundBacteriophage or phage is a type of virus that replicates itself inside bacteria. It consist of genetic material surrounded by a protein structure. Bacteriophage plays a vital role in the domain of phage therapy and genetic engineering. Phage and hydrolases enzyme proteins have a significant impact on the cure of pathogenic bacterial infections and disease treatment. Accurate identification of bacteriophage proteins is important in the host subcellular localization for further understanding of the interaction between phage, hydrolases, and in designing antibacterial drugs. Looking at the significance of Bacteriophage proteins, besides wet laboratory-based methods several computational models have been developed so far. However, the performance was not considerable due to inefficient feature schemes, redundancy, noise, and lack of an intelligent learning engine. Therefore we have developed an anovative bi-layered model name DeepEnzyPred. A Hybrid feature vector was obtained via a novel Multi-Level Multi-Threshold subset feature selection (MLMT-SFS) algorithm. A two-dimensional convolutional neural network was adopted as a baseline classifier.ResultsA conductive hybrid feature was obtained via a serial combination of CTD and KSAACGP features. The optimum feature was selected via a Novel Multi-Level Multi-Threshold Subset Feature selection algorithm. Over 5-fold jackknife cross-validation, an accuracy of 91.6 %, Sensitivity of 63.39%, Specificity 95.72%, MCC of 0.6049, and ROC value of 0.8772 over Layer-1 were recorded respectively. Similarly, the underline model obtained an Accuracy of 96.05%, Sensitivity of 96.22%, Specificity of 95.91%, MCC of 0.9219, and ROC value of 0.9899 over layer-2 respectivily.ConclusionThis paper presents a robust and effective classification model was developed for bacteriophage and their types. Primitive features were extracted via CTD and KSAACGP. A novel method (MLMT-SFS ) was devised for yielding optimum hybrid feature space out of primitive features. The result drew over hybrid feature space and 2D-CNN shown an excellent classification. Based on the recorded results, we believe that the developed predictor will be a valuable resource for large scale discrimination of unknown Phage and hydrolase enzymes in particular and new antibacterial drug design in pharmaceutical companies in general.

scholarly journals Adaptive Multi-level Backward Tracking for Sequential Feature Selection

2021 ◽  
Vol 15 (1) ◽  
pp. 1-20
Author(s):  
Knitchepon Chotchantarakun ◽  
Ohm Sornil
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Nearest Neighbor ◽  
Second Phase ◽  
K Nearest Neighbor ◽  
Backward Tracking ◽  
Sequential Feature Selection ◽  
Multi Level ◽  
A New Technique ◽  
Two Phases

In the past few decades, the large amount of available data has become a major challenge in data mining and machine learning. Feature selection is a significant preprocessing step for selecting the most informative features by removing irrelevant and redundant features, especially for large datasets. These selected features play an important role in information searching and enhancing the performance of machine learning models. In this research, we propose a new technique called One-level Forward Multi-level Backward Selection (OFMB). The proposed algorithm consists of two phases. The first phase aims to create preliminarily selected subsets. The second phase provides an improvement on the previous result by an adaptive multi-level backward searching technique. Hence, the idea is to apply an improvement step during the feature addition and an adaptive search method on the backtracking step. We have tested our algorithm on twelve standard UCI datasets based on k-nearest neighbor and naive Bayes classifiers. Their accuracy was then compared with some popular methods. OFMB showed better results than the other sequential forward searching techniques for most of the tested datasets.

Fault diagnosis for planetary gearboxes using multi-criterion fusion feature selection framework

2012 ◽  
Vol 227 (9) ◽  
pp. 2064-2076 ◽  
Author(s):  
Zhiliang Liu ◽  
Ming J Zuo ◽  
Hongbing Xu
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Failure Modes ◽  
Comprehensive Evaluation ◽  
Classification Performance ◽  
Planetary Gearbox ◽  
Correlation Criterion ◽  
Selection Framework ◽  
Fusion Framework ◽  
Fusion Feature

Feature selection has been used to achieve dimension reduction in the field of fault diagnosis. This article introduces a multi-criterion fusion framework for feature selection that takes into account three aspects of features: effectiveness, correlation, and classification performance. This framework enables a more comprehensive evaluation of features than does a single criterion. The proposed framework is implemented using five effectiveness criteria and a correlation criterion. It is used to diagnose eight failure modes of a planetary gearbox. The experimental results demonstrate that the proposed multi-criterion framework outperforms many well-studied single criteria.

A Simplified Cascaded Modular Structure for the Implementation of MVDR Detector

2011 ◽  
Vol 128-129 ◽  
pp. 491-494
Author(s):  
Zhong Jie Li ◽  
Cui Tao Zhu ◽  
Shao Ping Chen
Keyword(s):  
Computer Simulation ◽  
Interference Cancellation ◽  
Cross Correlation ◽  
Weight Vector ◽  
Modular Structure ◽  
Maximum Magnitude ◽  
Asymptotic Condition ◽  
Correlation Criterion ◽  
The Cross

A cascaded modular structure is proposed to implement the blind MVDR detector. In each module of the structure, a vector filter is introduced for adaptive interference cancellation. The weight vector is determined based on a maximum magnitude cross correlation criterion which maximizes the magnitude of the cross correlation between the output of the nonadaptive filter and that of the weight vector filter. The performance of the proposed receiver has been evaluated via computer simulation and shown to be comparable to that of the optimum method under asymptotic condition. When the number of received vectors is non-ideal, the proposed method outperform the optimum method.

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