Dimensionality Of Feature Set Research Articles

Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes

A promising approach for scalable Gaussian processes (GPs) is the Karhunen-Loève (KL) decomposition, in which the GP kernel is represented by a set of basis functions which are the eigenfunctions of the kernel operator. Such decomposed kernels have the potential to be very fast, and do not depend on the selection of a reduced set of inducing points. However KL decompositions lead to high dimensionality, and variable selection thus becomes paramount. This paper reports a new method of forward variable selection, enabled by the ordered nature of the basis functions in the KL expansion of the Bayesian Smoothing Spline ANOVA kernel (BSS-ANOVA), coupled with fast Gibbs sampling in a fully Bayesian approach. It quickly and effectively limits the number of terms, yielding a method with competitive accuracies, training and inference times for tabular datasets of low feature set dimensionality. Theoretical computational complexities are O ( N P 2 ) in training and O ( P ) per point in inference, where N is the number of instances and P the number of expansion terms. The inference speed and accuracy makes the method especially useful for dynamic systems identification, by modeling the dynamics in the tangent space as a static problem, then integrating the learned dynamics using a high-order scheme. The methods are demonstrated on two dynamic datasets: a ‘Susceptible, Infected, Recovered’ (SIR) toy problem, along with the experimental ‘Cascaded Tanks’ benchmark dataset. Comparisons on the static prediction of time derivatives are made with a random forest (RF), a residual neural network (ResNet), and the Orthogonal Additive Kernel (OAK) inducing points scalable GP, while for the timeseries prediction comparisons are made with LSTM and GRU recurrent neural networks (RNNs) along with the SINDy package.

A machine learning-based approach for flames classification in industrial Heavy Oil-Fire Boilers

The burner combustion tuning is a complex problem that has been studied through flame monitoring and characterization. It has been observed that the flame electromagnetic spectrum and flickering contain specific flame information in combustion processes. This information is helpful for combustion stoichiometry tuning on burners. This paper described a method for selecting the best flame feature subset that can be computed from the scanner signal, in order to get the flame index and induce combustion stoichiometry on burners under specific combustion conditions. We propose a method for selecting a reduced subset with only the useful flame features for flame index classification. To extract the most relevant flame features we use a feature subset selection (FSS) algorithm and to determine the combustion state in burners, five flame indices were defined that represent the most common flame states in oil fuel-fired boilers. FSS includes complete, sequential, and random searches in order to eliminate redundant and noisy flame features to decrease the flame feature set dimension. A probabilistic neural network (PNN) algorithm was implemented for flame feature clustering. Signals from the actual flame scanner system and relevant variables from the boiler data acquisition system were used by the algorithms to calculate the burner flame index. A set of parametric tests was done in a heavy oil-fired boiler under well-known flame and index conditions to train and test the flame classifier. The results showed that only the four more relevant features are enough to classify flames with a good performance (92.3% accuracy), which is useful for burner combustion monitoring and optimization.

An Android Malware Detection Method Using Multi-Feature and MobileNet

Most of the existing static analysis-based detection methods adopt one or few types of typical static features for avoiding the problem of dimensionality and computational resource consumption. In order to further improve detecting accuracy with reasonable resource consumption, in this paper, a new Android malware detection model based on multiple features with feature selection method and feature vectorization method are proposed. Feature selection method for each type of features reduces the dimensionality of feature set. Weight-based feature vectorization method for API calls, intent and permission is designed to construct feature vector. Co-occurrence matrix-based vectorization method is proposed to vectorize opcode sequence. To demonstrate the effectiveness of our method, we conducted comprehensive experiments with a total of 30,000 samples. Experimental results show that our method outperforms state-of-the-art methods.

Automated facial expression recognition using novel textural transformation

Facial expressions demonstrate the important information about our emotions and show the real intentions. In this study, a novel texture transformation method using graph structures is presented for facial expression recognition. Our proposed method consists of five steps. First the face image is segmented and resized. Then the proposed graph-based texture transformation is used as feature extractor. The exemplar feature extraction is performed using the proposed deep graph texture transformation. The extracted features are concatenated to obtain one dimensional feature set. This feature set is subjected to maximum pooling and principle component analysis methods to reduce the number of features. These reduced features are fed to classifiers and we have obtained the highest classification accuracy of 97.09% and 99.25% for JAFFE and TFEID datasets respectively Moreover, we have used CK + dataset to obtain comparison results and our textural transformation based model yielded 100% classification accuracy on the CK + dataset. The proposed method has the potential to be employed for security applications like counter terrorism, day care, residential security, ATM machine and voter verification.

Ensemble feature extraction-based prediction of fetal arrhythmia using cardiotocographic signals

The classification of Cardiotocography (CTG) signal abnormalities is critical in the identification of fetal anomalies. The non-stationary nature of CTG, as well as the dataset asymmetry, make this classification task complicated. Manual CTG analysis interpretation can easily result in a low diagnosis rate, which is mainly caused by a variety of subjective variables. Hence, in this research a new Ensemble Feature Extraction (EFE) model is implemented to analyze the nonlinear cardiotocographic signals. First, the collected signal artifacts are removed with interpolation operation, then down sampled to eliminate the complexity during classification. Followed by, a new EFE module is applied to extract the features, here learning model SE-ResNet-50 (Squeeze and Excitation -ResNet-50) is applied to separate the features from the regular and Time-Frequency data transformed (via CWT transform) signal. Wavelet Packet Transform is exploited to extract T-F signals. Then the enhanced feature set dimension is lessened by using Linear Discriminant Analysis (LDA) model. Finally, the features are fused and fed to the XG-booster for classification, and the patient is advised to seek therapy based on the outcome. The proposed method achieved 96.34% accuracy, 96.7% precision, 97.3% recalls, and 95.64% Quality Index (QI) on two-class classifications. This ensemble feature extraction increases the classification results and proves very advantageous to cardiotocographic signal classification. The results of the experiments show how effective the suggested EFE is.

NIMG-85. RADIOMIC FEATURES PREDICTIVE OF RESPONSE IN HGG-TARGETING CAR-T THERAPY

Abstract SIGNIFICANCE Radiomics may improve precision medicine in CAR-T (Chimeric Antigen Receptor T Cell) therapy patient selection. BACKGROUND High-Grade Glioma (HGG) is a heterogenous primary CNS neoplasm with a high recurrence rate and poor outcomes. Many studies are exploring CAR T cells to combat HGG. Radiomic models have shown value in identifying biomarkers predictive of tumor genetics, response, and patient prognosis. In this study, we explore radiomic features derived from four clinical sequences and volumes of edema and enhancing tumor to predict treatment response to the first three doses of CAR-T therapy. METHODS In this IRB-approved phase 1 clinical trial (IRB 13384), patients underwent surgical resection of the tumor and received CAR-T cell therapy. Of the 82 patients accrued, 59 (20 females, median age = 49) completed three cycles of therapy and had 3T field strength MRI scans with minimal imaging artifacts. T1 weighted pre-contrast, T1 weighted post-contrast, T2 weighted, and T2 FLuid Attenuated Inversion Recovery sequences were used to generate 3D and 2D radiomics features. In total 28,541 radiomic features were generated per patient using the images prior to CAR-T administration. Each patient’s response to treatment after three cycles was determined to be either stable disease (29 patients) or progression. The radiomic feature set dimensionality was reduced using Maximum Relevance Minimum Redundancy. 10-fold cross-validation XGBoost was used to determine radiomic features predictive of treatment response with a randomized grid search for hyperparameter tuning. RESULTS Six radiomic features (four shape-based), had high SHapley Additive exPlanations (SHAP)-based importance feature predictive of RANO response with an AUC >0.73. CONCLUSION Despite the limited study size, such imaging-based radiomic models can serve as a potential basis for optimizing clinical trial design through more precise patient screening and providing potential predictive imaging biomarkers of whether a patient will respond to CAR-T cell therapy in HGGs.

Multiclass feature selection with metaheuristic optimization algorithms: a review.

Selecting relevant feature subsets is vital in machine learning, and multiclass feature selection is harder to perform since most classifications are binary. The feature selection problem aims at reducing the feature set dimension while maintaining the performance model accuracy. Datasets can be classified using various methods. Nevertheless, metaheuristic algorithms attract substantial attention to solving different problems in optimization. For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately. Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. Further, lists of metaheuristic algorithms were introduced in the categories mentioned. In finding the solution to issues related to multiclass feature selection, only articles on metaheuristic algorithms used for multiclass feature selection problems from the year 2000 to 2022 were reviewed about their different categories and detailed descriptions. We considered some application areas for some of the metaheuristic algorithms applied for multiclass feature selection with their variations. Popular multiclass classifiers for feature selection were also examined. Moreover, we also presented the challenges of metaheuristic algorithms for feature selection, and we identified gaps for further research studies.

Automated classification of valvular heart diseases using FBSE-EWT and PSR based geometrical features

The growing prevalence and high mortality rate due to valvular heart diseases (VHD) are concerned. Therefore, its accurate, rapid, and early diagnosis is important. This study processes the phonocardiogram (PCG) signals for automatic detection of VHD associated with aortic stenosis (AS), mitral stenosis (MS), mitral valve prolapse (MVP) and mitral regurgitation (MR) which is originated by the MVP. In the proposed approach, we have used Fourier-Bessel series expansion-based empirical wavelet transform (FBSE-EWT) for capturing the non-stationary nature of PCG signals. It follows by selecting significant Fourier-Bessel intrinsic mode functions (FBIMFs) and extraction of geometrical features from two-dimensional phase space reconstruction (2D-PSRs) of the FBIMFs. To reduce the real-time processing load, the feature set dimension is reduced by using metaheuristics optimization-based features selection (FS) algorithms, namely, the salp swarm optimization algorithm (SSOA), emperor penguin optimization algorithm (EPOA), and tree growth optimization algorithm (TGOA). These FS methods have been tested and compared with machine learning classifiers. The result indicates the effectivity of FBSE-EWT based features extraction and used FS methods in classifying the intended categories of PCG signals. With the reduced features set, obtained with SSOA, the proposed approach has resulted in the highest classification accuracies of 98.53 %, 98.84 %, 99.07 % and 99.70 % respectively for the five classes, four classes, three classes, and two classes problems. Thus, besides aiding the cardiologists, this approach is also useful for developing wearable cardiac devices (as it uses a reduced feature set) for heart health monitoring and diagnosis purposes.

Degradation state identification for hydraulic pumps using modified hierarchical decomposition and image processing

Monitoring the degradation state of hydraulic pumps is of great significance to the safe and stable operation of equipment. As an important step, feature extraction has always been challenging. The non-stationary and nonlinear characteristics of vibration signals are likely to weaken the performance of traditional features. The two-dimensional image representation of vibration signals can provide more information for feature extraction, but it is challenging to obtain sufficient information based on small-size images. To solve these problems, a method for feature extraction based on modified hierarchical decomposition (MHD) and image processing is proposed in this paper. First, a set of signals decomposed by MHD are converted into gray-scale images. Second, features from accelerated segment test (FAST) algorithm are applied to detecting the feature points of the gray-scale image. Third, the real part of Gabor filter bank is used to convolve the images, and the responses of feature points are used to calculate histograms that are regarded as feature vectors. The method for feature extraction fully acquires the multi-layered texture information of small-size images and removes the redundant information. Furthermore, support vector machine (SVM) and nondominated sorting genetic algorithm II (NSGA-II) are introduced to conduct feature selection and state identification. NSGA-II and SVM can conduct the joint optimization of these two goals. The details of the proposed method are validated using experimental data, and the results show that the highest recognition rate of our proposed method can reach 100%. The results of the comparison among the proposed method, local binary pattern (LBP), and one-dimensional ternary patterns (1D-TPs) certify the superiorities of the proposed method. It obtains the highest classification accuracy (99.7%–98%) and the lowest feature set dimension (13–10).

Muscle Synergy of Lower Limb Motion in Subjects with and without Knee Pathology.

Surface electromyography (sEMG) has great potential in investigating the neuromuscular mechanism for knee pathology. However, due to the complex nature of neural control in lower limb motions and the divergences in subjects’ health and habits, it is difficult to directly use the raw sEMG signals to establish a robust sEMG analysis system. To solve this, muscle synergy analysis based on non-negative matrix factorization (NMF) of sEMG is carried out in this manuscript. The similarities of muscle synergy of subjects with and without knee pathology performing three different lower limb motions are calculated. Based on that, we have designed a classification method for motion recognition and knee pathology diagnosis. First, raw sEMG segments are preprocessed and then decomposed to muscle synergy matrices by NMF. Then, a two-stage feature selection method is executed to reduce the dimension of feature sets extracted from aforementioned matrices. Finally, the random forest classifier is adopted to identify motions or diagnose knee pathology. The study was conducted on an open dataset of 11 healthy subjects and 11 patients. Results show that the NMF-based sEMG classifier can achieve good performance in lower limb motion recognition, and is also an attractive solution for clinical application of knee pathology diagnosis.

Degradation State Identification for Hydraulic Pumps Based on Multi-scale Ternary Dynamic Analysis, NSGA-II and SVM

Abstract Degradation state identification for hydraulic pumps is crucial to ensure system performance. As an important step, feature extraction has always been challenging. The non-stationary and non-Gaussian characteristics of the vibration signal are likely to weaken the performance of traditional features. In this paper, an efficient feature extraction algorithm named multi-scale ternary dynamic analysis (MTDA) is proposed. MTDA reconstructs the phase space based on the given signal and converts each embedding vector into a ternary pattern independently, which enhances its capacity of describing the details of non-stationary signals. State entropy (SE) and state transition entropy (STE) are calculated to estimate the dynamical changes and complexity of each signal sample. The excellent performance of SE and STE in detecting frequency changes, amplitude changes, and the development process of fault is verified with the use of four simulated signals. The proposed multi-scale analysis enables them to provide a more precise estimation of entropy. Furthermore, support vector machine (SVM) and nondominated sorting genetic algorithm II (NSGA-II) are introduced to conduct feature selection and state identification. NSGA-II and SVM can conduct the joint optimization of these two goals. The details of the method proposed in this paper are tested using simulated signals and experimental data, and some studies related to the fault diagnosis of rotating machinery are compared with our method. All the results show that our proposed method has better performance, which obtains higher recognition accuracy and lower feature set dimension.

Detecting Unfavorable Driving States in Electroencephalography Based on a PCA Sample Entropy Feature and Multiple Classification Algorithms.

Unfavorable driving states can cause a large number of vehicle crashes and are significant factors in leading to traffic accidents. Hence, the aim of this research is to design a robust system to detect unfavorable driving states based on sample entropy feature analysis and multiple classification algorithms. Multi-channel Electroencephalography (EEG) signals are recorded from 16 participants while performing two types of driving tasks. For the purpose of selecting optimal feature sets for classification, principal component analysis (PCA) is adopted for reducing dimensionality of feature sets. Multiple classification algorithms, namely, K nearest neighbor (KNN), decision tree (DT), support vector machine (SVM) and logistic regression (LR) are employed to improve the accuracy of unfavorable driving state detection. We use 10-fold cross-validation to assess the performance of the proposed systems. It is found that the proposed detection system, based on PCA features and the cubic SVM classification algorithm, shows robustness as it obtains the highest accuracy of 97.81%, sensitivity of 96.93%, specificity of 98.73% and precision of 98.75%. Experimental results show that the system we designed can effectively monitor unfavorable driving states.

MI-LFGOA: multi-island levy-flight based grasshopper optimization for spatial image steganalysis

A few rich models of steganalysis have been developed that produce high dimensional feature sets for good detection accuracy with high computational cost and time. This paper inspires by the multi-layered concept with a metaheuristic method for reducing the dimensions of feature set and computational cost with maintained detection accuracy. We use Grasshopper Optimization algorithm (GOA) as a baseline for feature reduction, due to its advantage of giving good global optima. While for improving random walk of grasshoppers for balancing local and global solution search in parallel, we use Levy-Flight to modify the GOA which is named as Levy-Flight Grasshopper Optimization algorithm (LFGOA). To reduce the general redundant features without affecting detection accuracy, we preprocess the original feature set with PCA followed by LFGOA for further reduction in dimensions of resultant feature set with improved detection accuracy. This same proposed model is executed at different levels (Multi-Islands) with a different set of the population to get better results than single level LFGOA. The proposed framework is named as Multi-Island Levy Flight Grasshopper Optimization (MI-LFGOA). On the ground of BOSS base 1.01 image database which consists of 10,000 grayscale images, we investigate the proposed steganalysis method for two rich model’s feature sets (34671-D SRM and 686-D SPAM) with different classifiers. The experimental results show that MI-LFGOA is a promising method which achieves 92% to 96% reduction in features dimension while the detection accuracy of the steganalysis is maintained in comparison to other feature selection methods for steganalysis.

Efficient Hand Movement Detection Using k-Means Clustering and k-Nearest Neighbor Algorithms

Electromyography (EMG) signals are commonly used in prosthetic limb studies. We have proposed a system to detect six basic hand movements using unsupervised and supervised classification algorithms. In this study, two-channel EMG recordings belonging to six different hand movements are analyzed and the performance of the wavelet-based features for hand movement clustering and classification are examined for six subjects (three females and three males). The approximation and detail components are obtained by four-level symmetric wavelet transform. The energy, mean, standard deviation, and entropy values of the wavelet components are calculated and the feature sets are generated. After feature extraction, feature set dimensionality is reduced using principal component analysis, and then the k-nearest neighbor method and k-means clustering are applied for classification and clustering, respectively. The analyses are performed subject-specifically and gender-specifically. Thus, it is possible to evaluate the gender effect on classification performances. Subject-specific hand movements were detected with accuracy in the range of 86.33–100%. Gender-specific hand movements were detected with an accuracy of 96.67% for males and 92.78% for females. The classification and clustering results support each other. It was observed that the samples of hand movements that were classified incorrectly were concentrated in the same clusters. Similarly, it was found that the hand movements that were easily detected were homogeneously clustered.

Automated Surgical Term Clustering: A Text Mining Approach for Unstructured Textual Surgery Descriptions.

High costs in health care and everlasting need for quality improvement in care delivery is increasingly becoming the motivating factor for novel predictive studies in health care informatics. Surgical services impact both the operating theatre costs and revenues and play critical role in care quality. Efficiency of such units relies extremely on effective operational planning and inventory management. A key ingredient to such planning activities is the structured and unstructured data available prior to the surgery day from the electronic health records and other information systems. Unstructured data, such as textual features of procedure description and notes, provide additional information while structured data alone is not sufficient. To effectively utilize textual information using text mining, textual features should be easily identifiable, i.e., without typographical errors and ad hoc abbreviations. While there exists numerous spelling correction and abbreviation identification tools, they are not suitable for the surgical medical text as they require a dictionary and cannot accommodate ad hoc words such as abbreviations. This study proposes a novel preprocessing framework for surgical text data to detect misspellings and abbreviations prior to the application of any text mining and predictive modeling. The proposed approach helps extract the most salient text features from the unstructured principal procedure and additional notes by effectively reducing the raw feature set dimension. The transformed (text) feature set thus improves subsequent prediction tasks in surgery units. We test and validate the proposed approach using datasets from multiple hospitals' surgical departments and benchmark feature sets.

Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models.

Cardiotocography (CTG) consists of two biophysical signals that are fetal heart rate (FHR) and uterine contraction (UC). In this research area, the computerized systems are usually utilized to provide more objective and repeatable results. Feature selection algorithms are of great importance regarding the computerized systems to not only reduce the dimension of feature set but also to reveal the most relevant features without losing too much information. In this paper, three filters and two wrappers feature selection methods and machine learning models, which are artificial neural network (ANN), k-nearest neighbor (kNN), decision tree (DT), and support vector machine (SVM), are evaluated on a high dimensional feature set obtained from an open-access CTU-UHB intrapartum CTG database. The signals are divided into two classes as normal and hypoxic considering umbilical artery pH value (pH < 7.20) measured after delivery. A comprehensive diagnostic feature set forming the features obtained from morphological, linear, nonlinear, time-frequency and image-based time-frequency domains is generated first. Then, combinations of the feature selection algorithms and machine learning models are evaluated to achieve the most effective features as well as high classification performance. The experimental results show that it is possible to achieve better classification performance using lower dimensional feature set that comprises of more related features, instead of the high-dimensional feature set. The most informative feature subset was generated by considering the frequency of selection of the features by feature selection algorithms. As a result, the most efficient results were produced by selected only 12 relevant features instead of a full feature set consisting of 30 diagnostic indices and SVM model. Sensitivity and specificity were achieved as 77.40% and 93.86%, respectively. Consequently, the evaluation of multiple feature selection algorithms resulted in achieving the best results.

Steganographic Tool Detection using Specific Composite Feature Set and Weighted Decision Function

Steganographic tools available in the internet and other commercial steganographic tools are preferred than customized steganographic tools developed from scratch by unlawful groups. Hence a clue regarding the steganographic tool deployed in the covert communication process can save time for the steganalyst in the crucial active steganalysis phase. Signature analysis can lead to success in targeted steganalysis but tool detection needs to be taken forward from a point with a suspicious stego image in hand with no additional details available. In such scenarios, statistical steganalysis comes to rescue but with issues to be addressed like huge dimensionality of feature sets and complex ensemble classifiers. This work accomplishes tool detection with a specific composite feature set identified to distinguish one stego tool from the others with a weighted decision function to enhance the role of the specific feature set when it votes for a particular class. A tool detection accuracy of 85.25% has been achieved simultaneously addressing feature set dimensionality and complexity of ensemble classifiers and a comparison with a benchmark procedure has been made

A cluster-based feature selection method for image texture classification

&lt;p&gt;Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.&lt;/p&gt;

Computer-aided diagnosis of gastrointestinal stromal tumors: a radiomics method on endoscopic ultrasound image.

The purpose of our study is to propose a preoperative computer-aided diagnosis system based on a radiomics method to differentiate gastrointestinal stromal tumors (GISTs) of the higher-risk group (HRG) from those of the lower-risk group (LRG) on endoscopic ultrasound (EUS) images. Gastro-EUS (G-EUS) images of four different risk level GISTs were collected from 19 hospitals. The datasheet included 168 case HRG GISTs and 747 case LRG GISTs. A radiomics method with image segmentation, feature extraction, feature selection and classification was developed. Here 439 radiomics features were firstly extracted, and then, the least absolute shrinkage selection operator (lasso) model with a tenfold cross-validation and 31 bootstraps was used to reduce the dimension of feature sets. Finally, random forest was applied to establish the classification model. The proposed model differentiated 32 case HRG GISTs from 149 case LRG GISTs. Result for the testing set achieved the area under the receiver operating characteristic curve of 0.839, the accuracy of 0.823, the sensitivity of 0.813 and the specificity of 0.826. The model could increase preoperative diagnostic accuracy and provide a valuable reference for the doctors.

A support vector machine approach to detect trans-tibial prosthetic misalignment using 3-Dimensional ground reaction force features: A proof of concept

Prosthetists conventionally evaluate alignment based on visual interpretation of patients' gait, which is convenient, but largely subjective and depends on prosthetists' experience. In this paper, we explore the feasibility of using a support vector machine (SVM) approach to automatically detect misalignment of trans-tibial prostheses through ground reaction force (GRF). Alternate classification algorithms with varying kernels and feature sets were compared to assess the suitability for detection of a representative misalignment (six degrees of ankle plantar flexion) from normal alignment. A classical feature selection algorithm, Fisher Score, was further introduced to identify valuable features and reduce the dimension of feature sets. The SVMs achieved a detection accuracy of 96.67% at best within the same subject and 88.89%, respectively, for inter-subject. Combined horizontal and vertical components of GRF features provided the maximum detection accuracies. Propulsion peak force was identified as key variable of gait for misalignment prediction. As a proof of concept, the results demonstrate potential in applying this approach to detect prosthetic misalignment based on gait patterns, and is a step towards future developments of tools for early prevention of misalignment in clinical.