IAmyP: A Multi-view Learning for Amyloidogenic Hexapeptides Identification Based on Sequence Least Squares Programming.

The main task of intrusion detection is to extract meaningful and effective features from redundant and noisy features causing poor detection accuracy. A method of feature selection by Kernel Principle Component Analysis (KPCA) and fusion is proposed. The basic features, contend features and traffic features are extracted respectively by KPCA. Then we use two levels of fusion (feature fusion and decision fusion) technique to improve the performance of intrusion detection system. However, simple combining features will not work as well as expected. For this reason, a new feature fusion method, IS feature fusion is presented. Experiments have been done on dataset in KDD-99 and simulation results show that our method by KPCA feature selection is an effective and IS feature fusion outperforms other fusion techniques.

Different protection states have different protection effects on bogie traction motor bearings under road transportation conditions, which directly affects the stability and safety of later vehicle operations. The motor bearing monitoring system usually adopts the time-frequency domain and other indicators to accurately describe its state, and the selection of specific indicators usually originates from the experience of domain experts. To explore the weak and effective features hidden in the data more deeply, this paper researches feature selection and fusion, and proposes a hybrid multi-measure and improved UMAP approach for train traction motor bearing protection state assessment. Firstly, a platform is built to collect multiple sensor signals of traction motor vibration during road transportation and the time-frequency domain feature set is extracted. Then, by comparing the feature extraction effects of 10 single-measure models, a hybrid multi-measure feature selection model and its evaluation indexes, which comprehensively consider the combined distance measure, correlation, and information measure, are constructed to optimize the best hybrid multimetric and the feature set corresponding to the optimal strategy. To perform multi-sensor feature fusion, the PSO-optimized UMAP algorithm is used for feature fusion of the feature set and compared with eight other algorithms. Finally, the results of feature selection and fusion are input into eight classifiers for performance comparison, and the method achieves more than 85% accuracy in distinguishing different protection states, obviously due to the original feature subset. The experimental results prove the effectiveness of the method proposed in this paper, and it provides a theoretical basis and technical reference for the protection scheme of the traction motor and the design and selection of bearings.

Proteins are essential for the vital functions of living organisms, playing key roles in various cellular processes. Accurate prediction of protein structures is crucial, with an important step involving the identification of the structural fold of an unknown protein by comparing its sequence to known protein folds. These known folds, particularly those experimentally validated, serve as templates for modeling the structure of the unknown protein. This process, known as Protein Fold Recognition (PFR), has remained a longstanding challenge. Existing PFR methods typically use mathematical techniques that incorporate evolutionary information through Evolutionary Profiles. In this study, we propose enhancing PFR performance by integrating evolutionary information with the attention mechanism from transformer-based models. We evaluate our approach using four widely-used protein datasets: DD, EDD, TG, and the latest SCOPe version (2.08), with four proposed models. Among these, the best-performing model achieves impressive accuracies of 93.10%, 96.40%, 93.30%, and 92.24% for the respective datasets. Three of the models incorporate ESM-2, a large language model pre-trained on protein data. A comprehensive performance analysis demonstrates that the fusion of attention-based features with evolutionary features yields the most effective classification results. Notably, the inclusion of evolutionary features boosts the accuracy of ESM-2 by 6-10% on the DD, EDD, and TG datasets, while a significant increase of over 5% is observed for the SCOPe 2.08 dataset. This work marks a notable progression in Protein Fold Recognition by combining evolutionary features with attention mechanisms, leading to improved prediction accuracy.

Improved Pest-YOLO: Real-time pest detection based on efficient channel attention mechanism and transformer encoder

Rationale and ObjectivesAccurate prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for guiding treatment. This study evaluates and compares the performance of clinicoradiologic, traditional radiomics, deep learning radiomics, feature fusion, and decision fusion models based on multi-region MR habitat imaging using six machine learning classifiers. Materials and MethodsWe retrospectively included 300 HCC patients. The intratumoral and peritumoral regions were segmented into distinct habitats, from which radiomics and deep learning features were extracted using arterial phase MR images. To reduce feature dimensionality, we applied intra-class correlation coefficient (ICC) analysis, Pearson correlation coefficient (PCC) filtering, and recursive feature elimination (RFE). Based on the selected optimal features, prediction models were constructed using decision tree (DT), K-nearest neighbors (KNN), logistic regression (LR), random forest (RF), support vector machine (SVM), and XGBoost (XGB) classifiers. Additionally, fusion models were developed utilizing both feature fusion and decision fusion strategies. The performance of these models was validated using the area under the receiver operating characteristic curve (ROC AUC), calibration curves, and decision curve analysis. ResultsThe decision fusion model (VOI-Peri10-1) using LR and integrating clinicoradiologic, radiomics, and deep learning features achieved the highest AUC of 0.808 (95% confidence interval [CI]: 0.807–0.912) in the test cohort, with good calibration (Hosmer-Lemeshow test, P > 0.050) and clinical net benefit. ConclusionThe LR-based decision fusion model integrating clinicoradiologic, radiomics, and deep learning features shows promise for preoperative prediction of MVI in HCC, aiding in patient outcome predictions and personalized treatment planning.

Sentiment analysis, a crucial aspect of Natural Language Processing (NLP), plays a pivotal role in understanding public opinion, customer feedback, and user sentiments in various domains. In this study, we present a comprehensive approach to sentiment analysis that incorporates both textual and emoji data, leveraging diverse datasets from sources such as social media, customer reviews, and surveys. Our methodology consists of several key steps, including data collection, pre-processing, feature extraction, feature fusion, and feature selection. For data pre-processing, we apply techniques such as tokenization, lowercasing, stop word removal, and stemming to ensure uniform and meaningful text representation. We also extract emojis from text using regular expressions and convert them into textual representations, facilitating unified processing. Text data is transformed into feature vectors using Term frequency_ Inverse Term frequency (TF-IDF) weighting-based Bag of Words (TF-IDF_BoW) with word frequencies, pre-trained Word2Vec word embeddings, and n-grams to capture local word patterns. Emoji data, on the other hand, is processed using pre-trained emoji embeddings (Emoji2Vec) and emoji frequency counting. The Feature Fusion stage involves the combination of text and emoji features into a single feature vector, with a weighted concatenation approach. In the Feature Selection phase, introduced a Self-Improved Siberian Tiger Optimization (SI-STO), a novel feature selection technique, to identify the most relevant features for sentiment prediction. The sentiment classification model SentEmoNet combines LSTM with an Attention Mechanism and a CNN for capturing text patterns and extracting local features from text and emoji embeddings. We use standard sentiment analysis metrics to assess performance in the model evaluation phase. The proposed model has achieved an accuracy of 97.87%, which is better than the existing ones.

Multimodal Sarcasm Detection (MSD) is crucial for understanding complex human communication and building intelligent emotional computing systems. However, existing MSD methods often over-rely on spurious correlations, causing the learned features to deviate from the true semantics of sarcasm. This bias significantly undermines the generalization ability of current models beyond the training environment. This paper proposes a Confict-based Disentangled multi-view incongruity learning framework (ConDi), aiming to effectively disentangle and interact with heterogeneous features in multimodal sarcasm. Given that multimodal embedding spaces are typically heterogeneous, direct fusion can disrupt the inherent structure of embeddings from different modalities. To address this issue, we employ the optimal transport algorithm to align embeddings from different modalities into a unified space. Subsequently, we jointly learn incongruities from three views: modality disentanglement, global sentiment, and local description. To achieve a debiased fusion of sarcasm features, we design a conflict-based fusion module to integrate features from these three views. Experimental results demonstrate the superiority of ConDi on multimodal sarcasm datasets, and further analysis shows that ConDi can effectively reduce reliance on spurious correlations. Additionally, out-of distribution (OOD) experiments reveal that ConDi achieves better generalization.

Prediction of the stage of cancer plays an important role in planning the course of treatment and has been largely reliant on imaging tools which do not capture molecular events that cause cancer progression. Gene-expression data-based analyses are able to identify these events, allowing RNA-sequence and microarray cancer data to be used for cancer analyses. Breast cancer is the most common cancer worldwide, and is classified into four stages - stages 1, 2, 3, and 4 [2]. While machine learning models have previously been explored to perform stage classification with limited success, multi-class stage classification has not had significant progress. There is a need for improved multi-class classification models, such as by investigating deep learning models. Gene-expression-based cancer data is characterised by the small size of available datasets, class imbalance, and high dimensionality. Class balancing methods must be applied to the dataset. Since all the genes are not necessary for stage prediction, retaining only the necessary genes can improve classification accuracy. The breast cancer samples are to be classified into 4 classes of stages 1 to 4. Invasive ductal carcinoma breast cancer samples are obtained from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets and combined. Two class balancing techniques are explored, synthetic minority oversampling technique (SMOTE) and SMOTE followed by random undersampling. A hybrid feature selection pipeline is proposed, with three pipelines explored involving combinations of filter and embedded feature selection methods: Pipeline 1 - minimum-redundancy maximum-relevancy (mRMR) and correlation feature selection (CFS), Pipeline 2 - mRMR, mutual information (MI) and CFS, and Pipeline 3 - mRMR and support vector machine-recursive feature elimination (SVM-RFE). The classification is done using deep learning models, namely deep neural network, convolutional neural network, recurrent neural network, a modified deep neural network, and an AutoKeras generated model. Classification performance post class-balancing and various feature selection techniques show marked improvement over classification prior to feature selection. The best multiclass classification was found to be by a deep neural network post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with a Cohen-Kappa score of 0.303 and a classification accuracy of 53.1%. For binary classification into early and late-stage cancer, the best performance is obtained by a modified deep neural network (DNN) post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with an accuracy of 81.0% and a Cohen-Kappa score (CKS) of 0.280. This pipeline also showed improved multiclass classification performance on neuroblastoma cancer data, with a best area under the receiver operating characteristic (auROC) curve score of 0.872, as compared to 0.71 obtained in previous work, an improvement of 22.81%. The results and analysis reveal that feature selection techniques play a vital role in gene-expression data-based classification, and the proposed hybrid feature selection pipeline improves classification performance. Multi-class classification is possible using deep learning models, though further improvement particularly in late-stage classification is necessary and should be explored further.

The present work involves statistically analyzing and studying the overall classification accuracy results using Hue channel images of different plant species using their dorsal and ventral sides, and then subjecting them to the process of feature extraction using first order statistical features and texture based features. These extracted features have been subjected to the classification process using KNN and Random Forest algorithms. Further, this work studies the fusion of two different kinds of features extracted for dorsal and ventral plant leaf images and studying the effect of fusion on the overall classification accuracy results. This work also delves into the feature selection task using random forest algorithm and studies the effect of reduced dataset with unique features on the overall classification accuracy results. The most important outcome of this investigation is that the ventral leaf images can be a suitable alternative for plant species classification using digital images and further, the fusion of features does improve the classification accuracy results.

DNA Microarrays are promising tool for cancer diagnosis and prognosis. DNA Microarrays are high-dimensional and gene selection is a difficult task. However, Bootstrap-based ensemble feature selection (Bagging) recently becomes popular and shows significant improvements in the field. This method aims to generate several slightly different sampled datasets, using bootstrap resampling, from training dataset. Afterwards, it aggregates all ranked feature lists, generated from sampled datasets, to obtain final (ensemble) feature list. Performance of bagging is proportional to diversity of generated sampled datasets. Therefore, it is proposed to use under-sampling of training set instead of using entire training set for bootstrap resampling to improve classification performance and gene selection stability. The proposed method was evaluated using support vector machine (SVM) as the classifier and recursive feature elimination (SVM-RFE) as the feature selection technique. Four microarray datasets were used for evaluation of the proposed method. The results show that 50% under-sampling approach have similar classification performance and outperforms conventional approach in terms of gene selection stability. In addition, 50% under-sampling uses only half of the samples of training dataset at each run of ensemble method so it has less computational cost.

PLS-based recursive feature elimination for high-dimensional small sample

Feature (or variable) selection is the process of identifying the minimal set of features with the highest predictive performance on the target variable of interest. Numerous feature selection algorithms have been developed over the years, but only few have been implemented in R and made publicly available R as packages while offering few options. The R package MXM offers a variety of feature selection algorithms, and has unique features that make it advantageous over its competitors: a) it contains feature selection algorithms that can treat numerous types of target variables, including continuous, percentages, time to event (survival), binary, nominal, ordinal, clustered, counts, left censored, etc; b) it contains a variety of regression models that can be plugged into the feature selection algorithms (for example with time to event data the user can choose among Cox, Weibull, log logistic or exponential regression); c) it includes an algorithm for detecting multiple solutions (many sets of statistically equivalent features, plain speaking, two features can carry statistically equivalent information when substituting one with the other does not effect the inference or the conclusions); and d) it includes memory efficient algorithms for high volume data, data that cannot be loaded into R (In a 16GB RAM terminal for example, R cannot directly load data of 16GB size. By utilizing the proper package, we load the data and then perform feature selection.). In this paper, we qualitatively compare MXM with other relevant feature selection packages and discuss its advantages and disadvantages. Further, we provide a demonstration of MXM’s algorithms using real high-dimensional data from various applications.

Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images

This paper describes the experiment of analyzing MRI images of the brain of patients with Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal control (NC). The aim of this research is to use new methods of extracting features from MRI images and selecting the most relevant features. The values of cortical thickness in regions of interest are used as a features for MRI images. The Support Vector Machine — Recursive Feature Elimination, Infinite Feature Selection, Relief-F, Feature Selection Via Concave Minimization and Support Vector Machine, Minimum Redundancy Maximum Relevance, Fisher, Unsupervised Discriminative Feature Selection, L0-norm Based Feature Selection, and Feature Selection via Eigenvector Centrality techniques for feature selection and ranking were used. In the experiment, the MRI images from ADNI database was used, which contains data from 539 subjects. In the result, the following maximum values of area under ROC curves for three datasets are obtained: NC-AD — 0.90, NC-MCI — 0.71, MCI-AD — 0.69. Classification accuracy with linear Support Vector Machine for the corresponding datasets: NC-AD — 80%, NC-MCI — 64%, MCI-AD — 65%. The results of this paper can be used for building a classifier in space of selected features.

We develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) in the classification of the pulmonary lesion and identify optimal machine learning methods. This retrospective analysis included 201 patients (143 malignancies, 58 benign lesions). Radiomics features were extracted from multiparametric MRI, including T2-weighted imaging (T2WI), T1-weighted imaging (TIWI), and apparent diffusion coefficient (ADC) map. Three feature selection methods, including recursive feature elimination (RFE), t test, and least absolute shrinkage and selection operator (LASSO), and three classification methods, including linear discriminate analysis (LDA), support vector machine (SVM), and random forest (RF) were used to distinguish benign and malignant pulmonary lesions. Performance was compared by AUC, sensitivity, accuracy, precision, and specificity. Analysis of performance differences in three randomly drawn cross-validation sets verified the stability of the results. For most single MR sequences or combinations of multiple MR sequences, RFE feature selection method with SVM classifier had the best performance, followed by RFE with RF. The radiomics model based on multiple sequences showed a higher diagnostic accuracy than single sequence for every machine learning method. Using RFE with SVM, the joint model of T1WI, T2WI, and ADC showed the highest performance with AUC = 0.88 ± 0.02 (sensitivity 83%; accuracy 82%; precision 91%; specificity 79%) in test set. Quantitative radiomics features based on multiparametric MRI have good performance in differentiating lung malignancies and benign lesions. The machine learning method of RFE with SVM is superior to the combination of other feature selection and classifier methods. • Radiomics approach has the potential to distinguish between benign and malignant pulmonary lesions. • Radiomics model based on multiparametric MRI has better performance than single-sequence models. • The machine learning methods RFE with SVM perform best in the current cohort.