Sequential Forward Floating Selection Algorithm Research Articles

Internet of things driven multilinear regression technique for fertilizer recommendation for precision agriculture

Food instability has been linked to infertility, health issues, accelerated aging, incorrect insulin regulation, and more. Innovative approaches increased food availability and quality. Agriculture environment monitoring systems need IoT and machine learning. IoT sensors provide all necessary data for agriculture production forecast, fertilizer management, smart irrigation, crop monitoring, crop disease diagnosis, and pest control. Precision agriculture may boost crop yields by prescribing the right water-fertilizer-paste ratio. This article presents IOT based fertilizer recommendation system for Smart agriculture. This framework uses IoT devices and sensors to acquire agriculture-related data, and then machine learning is applied to suggest fertilizer in the correct quantity and at the appropriate time. The data acquisition phase collects input data, including soil temperature, moisture, humidity, regions' weather data, and crop details. Features are selected using the Sequential Forward Floating Selection algorithm. Multilinear Regression performs data classification. The performance of SFSS-MLR is compared to Random Forest, C4.5, Naïve Bayes algorithm. SFSS MLR is better in accuracy, precision, recall and F1. The accuracy of SFSS MLR is 99.3 percent.

Research on Compressed Sensing Spectrometry Based on Electro-Optical Transmittance Coding

To develop fast and integrated spectrometry, we present a new method of compressed sensing spectrometry based on electro-optical transmittance coding. A LiNbO3 electro-optic modulator was applied to a directly compressed measurement code in the spectral dimension. Subsequently, a sequential forward floating selection algorithm was employed to select the coding measurement matrix, and sparsity adaptive matching pursuit was used as a solver algorithm. The principle was analyzed, and the measurement system was built for verification experiments. The experimental results reveal that the spectral reconstruction relative error is in the order of 10−2, and the full width at half maximum of the spectral measurement is as high as 1.2 nm. The spectral resolution can reach approximately 0.4 nm in the visible light band range (0.38–0.78 μm) with 1024 spectral channels. The compressed ratio of the compressed sensing spectrometry is up to 1:29. Furthermore, the spectral signal measurement time is only 0.25 ms. The study demonstrates a novel method, which exhibits high precision, speed, high compressed ratio and hyperspectral resolution.

A novel soft-coded error-correcting output codes algorithm

Error-Correcting Output Codes (ECOC) algorithms enable multiclass classification by reassigning multiple classes to the positive/negative group with the class reassignment schemes being recorded as binary/ternary hard-coded (HC) codematrices. Different classes tend to get diverse subordination degrees to the positive/negative group, providing clues to correct potential errors. However, the HC codematrices are unable to provide the information in the subordination degrees. In this paper, a Soft-Coded ECOC (SC-ECOC) scheme, namely, the Sequential Forward Floating Selection algorithm, is proposed by filling codematrices with real values instead of hard codes to improve classification performance. This algorithm divides multiple classes into two groups by maximizing the ratio of inter-group distance to intra-group distance. Then a new measure coverage is designed to evaluate the subordination degrees of different classes to both groups, which are set as the elements to form a codematrix. Furthermore, a self-adaptive strategy adjusts the value of each element to fit learners better. Experiments are carried out to verify the performance of our algorithm on various data sets, and results confirm that our algorithm can achieve more balanced results compared with the traditional HC ECOC algorithms. Besides, the values of soft codes correlate with the difficulty level of various classes to improve the multiclass classification ability.

Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains.

Multi-sensor fusion intends to boost the general reliability of a decision-making procedure or allow one sensor to compensate for others’ shortcomings. This field has been so prominent that authors have proposed many different fusion approaches, or “architectures” as we call them when they are structurally different, so it is now challenging to prescribe which one is better for a specific collection of sensors and a particular application environment, other than by trial and error. We propose an approach capable of predicting the best fusion architecture (from predefined options) for a given dataset. This method involves the construction of a meta-dataset where statistical characteristics from the original dataset are extracted. One challenge is that each dataset has a different number of variables (columns). Previous work took the principal component analysis’s first k components to make the meta-dataset columns coherent and trained machine learning classifiers to predict the best fusion architecture. In this paper, we take a new route to build the meta-dataset. We use the Sequential Forward Floating Selection algorithm and a T transform to reduce the features and match them to a given number, respectively. Our findings indicate that our proposed method could improve the accuracy in predicting the best sensor fusion architecture for multiple domains.

Generalized methodology for radiomic feature selection and modeling in predicting clinical outcomes

Background. Quantitative radiomic features of medical images could provide clinical significance in assisting decision-making, but the existing feature selection and modeling methods are usually parameter-dependent. We aim to develop and validate a generalized radiomic method applicable to a variety of clinical outcomes. Methods and materials. A generalized methodology for radiomic feature selection and modeling (‘GRFM’ for short), including two-step feature selection and logistic regression, was proposed for studying clinical outcomes correlations. The two-step feature selection consists of Pearson correlation analysis followed by a sequential forward floating selection algorithm to identify robust feature subsets. We also applied an adaptive searching strategy to systematically determine globally optimal parameters, rather than relying on preset parameters. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of three outcomes: lymph node metastasis of gastric cancer (GC), the five-year survival status of high-grade osteosarcoma (HOS), and the pathological grade of pancreatic neuroendocrine tumors (pNETs). Results. The optimal Pearson thresholds were 0.85, 0.80 and 0.75, and the optimal feature numbers were 11, 14 and 8 in GC, HOS and pNETs, respectively. The AUC values of the three predictive models combined with the corresponding parameters were 0.9017 versus 0.9026, 0.7652 versus 0.7113, and 0.8438 versus 0.8212 for the training and validation cohorts, showing promissing generality and classifier performance . Conclusion. The proposed method was helpful in predicting different clinical outcomes, and has potential application as a general and noninvasive prediction tool to guide clinical decision-making in various cancer sites.

Autism spectrum disorder detection using sequential minimal optimization‐support vector machine hybrid classifier according to history of jaundice and family autism in children

SummaryAutism spectrum disorder (ASD) is a neurodevelopmental disorder caused by several nervous system problems that change the function of the brain. It is either congenital or occurs in the early years of life. Although the cause of ASD is not known exactly, it is generally thought to be genetic. Today, the relationship of ASD with other diseases is under investigation. One of these diseases is jaundice. It has been shown in different medical studies that the probability of children, who were jaundiced in the newborn period and/or had a family history of autism, having this disorder is higher than the others. However, studies in this area using artificial intelligence techniques are limited. For this reason, in this study, the relationship between ASD with jaundice and/or family history of autism in children was emphasized by using current machine learning techniques and analyses. Datasets were established by digitizing verbal data obtained from children between the ages of 4 and 11: (1) subjects with a family history of autism, (2) subjects with a history of jaundice, (3) subjects with both history of jaundice and family autism, and (4) subjects with no conditions. Since the verbal datasets created by the answers from the children (with or without ASD) or the people around them were converted into numerical form with 0–1 coding, mathematical operations could be performed using these datasets. The four subgroups of data mentioned above were given to the sequential minimal optimization‐support vector machine hybrid classifier after they were separated into training and test data via the stratified cross‐validation method. The results were analyzed with various statistical parameters. In addition, the sequential forward floating selection algorithm was used to determine which features were not effective for ASD detection. Obtained results from all datasets can provide a new perspective for the literature. As a result, it was determined with a 100% success rate for dataset1 and dataset3. In addition, the ASD detection rate was calculated at 95.52% in children with a history of jaundice. Finally, considerable and meaningful interpretations were made about which features according to the history of jaundice and family autism for each dataset are more effective in ASD detection in children.

A linearly extendible multi-artifact removal approach for improved upper extremity EEG-based motor imagery decoding.

Non-invasive multichannel Electroencephalography (EEG) recordings provide an alternative source of neural information from which motor imagery (MI) patterns associated with limb movement intent can be decoded for use as control inputs for rehabilitation robots. The presence of multiple inherent dynamic artifacts in EEG signals, however, poses processing challenges for brain-computer interface (BCI) systems. A large proportion of the existing EEG signal preprocessing methods focus on isolating single artifact per time from an ensemble of EEG trials and require calibration and/or reference electrodes, resulting in increased complexity of their application to MI-EEG controlled rehabilitation devices in practical settings. Also, a few existing multi-artifacts removal methods though explored in other domains, they have rarely been investigated in the space of MI-EEG signals for multiple artifacts cancellation in a simultaneous manner. Building on the premise of previous works, this study propose a semi-automatic EEG preprocessing method that combines Generalized Eigenvalue Decomposition driven by low-rank approximation and a Multi-channel Wiener Filter (GEVD-MWF) that employs a learning technique for simultaneous elimination of multiple artifacts from MI-EEG signals. The proposed method is applied to remove multiple artifacts from 64-channel EEG signals recorded from transhumeral amputees while they performed distinct classes of upper limb MI tasks before decoding their movement intent using a selection of features and machine learning algorithms. Experimental results show that the proposed GEVD-MWF method yields significant improvements in MI decoding accuracies, in the range of 13.23%-41.21% compared to four existing popular artifact removal algorithms. Further investigation revealed that the GEVD-MWF approach enabled accuracies in the range of 90.44% - 99.67% using "single trial" EEG recordings, which could eliminate the need to record and process large ensembles of EEG trials as commonly required in some existing approaches. Additionally, using a variant of the sequential forward floating selection algorithm, a subset of 9 channels was used to obtain a decoding accuracy of 93.73%±1.58%. Given its improved performance, reduced data requirements, and feasibility with few channels, the proposed GEVD-MWF could potentially spur the development of effective real-time control strategies for multi-degree of freedom EEG-based miniaturized rehabilitation robotic interfaces.

Integrating tumor and nodal radiomics to predict lymph node metastasis in gastric cancer

BackgroundTo develop and validate a radiomics method via integrating tumor and lymph node radiomics for the preoperative prediction of lymph node (LN) status in gastric cancer (GC). Materials and methodsWe retrospectively collected 170 contrast-enhanced abdominal CT images from GC patients. Five times repeated random hold-out experiment was employed. Tumor and nodal radiomics features were extracted from each individual tumor and LN respectively, and then multi-step feature selection was performed. The optimal tumor and nodal features were selected using Pearson correlation analysis and sequential forward floating selection (SFFS) algorithm. After feature fusion, the SFFS algorithm was used to develop radiomics signatures. The performance of the radiomics signatures developed based on logistic regression classifier was further analyzed and compared using the area under the receiver operating characteristic curve (AUC). ResultsThe AUC values, reported as mean ± standard deviation, were 0.9319 ± 0.0129 and 0.8546 ± 0.0261 for the training and validation cohorts respectively. The radiomic signatures could predict LN status, especially in T2-stage, diffuse-type and moderately/well differentiated GC. After integrating clinicopathologic information, the radiomic-clinicopathologic model (training cohort, 0.9432 ± 0.0129; validation cohort, 0.8764 ± 0.0322) showed a better discrimination capability than other radiomics models and clinicopathologic model. The radiomic-clinicopathologic model also showed superior performance to the gastroenterologist’ decision in all experiments, and outperformed the radiologist in some experiments. ConclusionOur proposed method presented good predictive performance and great potential for predicting LNM in GC. As a noninvasive preoperative prediction tool, it can be helpful for guiding the prognosis and treatment decision-making in GC patients.

Gene expression feature selection for prostate cancer diagnosis using a two-phase heuristic-deterministic search strategy.

Here, a two-phase search strategy is proposed to identify the biomarkers in gene expression data set for the prostate cancer diagnosis. A statistical filtering method is initially employed to remove the noisiest data. In the first phase of the search strategy, a multi-objective optimisation based on the binary particle swarm optimisation algorithm tuned by a chaotic method is proposed to select the optimal subset of genes with the minimum number of genes and the maximum classification accuracy. Finally, in the second phase of the search strategy, the cache-based modification of the sequential forward floating selection algorithm is used to find the most discriminant genes from the optimal subset of genes selected in the first phase. The results of applying the proposed algorithm on the available challenging prostate cancer data set demonstrate that the proposed algorithm can perfectly identify the informative genes such that the classification accuracy, sensitivity, and specificity of 100% are achieved with only nine biomarkers.

Real-time Monitoring for Disk Laser Welding Based on Feature Selection and SVM

In order to automatically evaluate the welding quality during high-power disk laser welding, a real-time monitoring system was developed. The images of laser-induced metal vapor during welding were captured and fifteen features were extracted. A feature selection method based on a sequential forward floating selection algorithm was employed to identify the optimal feature subset, and a support vector machine (SVM) classifier was built to recognize the welding quality. The experiment results demonstrated that this method had satisfactory performance, and could be applied in laser welding monitoring applications.

Graphical Password-Based User Authentication With Free-Form Doodles

User authentication using simple gestures is now common in portable devices. In this work, authentication with free-form sketches is studied. Verification systems using dynamic time warping and Gaussian mixture models are proposed, based on dynamic signature verification approaches. The most discriminant features are studied using the sequential forward floating selection algorithm. The effects of the time lapse between capture sessions and the impact of the training set size are also studied. Development and validation experiments are performed using the DooDB database, which contains passwords from 100 users captured on a smartphone touchscreen. Equal error rates between 3% and 8% are obtained against random forgeries and between 21% and 22% against skilled forgeries. High variability between capture sessions increases the error rates.

Feature subset selection in dynamic stability assessment power system using artificial neural networks

This paper presents method of feature subset selection in dynamic stability assessment (DSA) power system using artificial neural networks (ANN). In the application of ANN on DSA power system, feature subset selection aims to reduce the number of training features, cost and memory computer. However, the major challenge is to reduce the number of features but classification rate gets a high accuracy. This paper proposes applying Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Sequential Forward Floating Selection (SFFS) and Feature Ranking (FR) algorithm to feature subset selection. The effectiveness of the algorithms was tested on the GSO-37bus power system. With the same number of features, the calculation results show that SFS algorithm yielded higher classification rate than FR, SBS algorithm. SFS algorithm yielded the same classification rate as SFFS algorithm.

Unsupervised Selection of Training Samples for Tree Species Classification Using Hyperspectral Data

In this study, we introduced a novel unsupervised selection method for collecting training samples for tree species classification at individual tree crown (ITC) level using hyperspectral data. The selection process is based on a distance metric defined among the spectral signatures of the pixels inside the ITCs, and a search strategy based on the Sequential Forward Floating Selection algorithm. The method was developed using two kinds of samples: plots and ITCs. The experimental results indicated that the method allows reducing the amount of training samples needed for the classification process, without significantly decreasing the classification accuracy. Applying the proposed method, the kappa accuracies obtained using about half of the total number of plots (kappa accuracy=0.84) and approximately one-third of the total number of ITCs (kappa accuracy=0.83) were not statistically different from the results obtained using the full set of training samples (kappa accuracy =0.86). The proposed method demonstrates that using a priori information derived from the hyperspectral data can substantially reduce the amount of field work and, consequently, the forest inventory costs.

Emotion classification during music listening from forehead biosignals

Emotion recognition systems are helpful in human–machine interactions and clinical applications. This paper investigates the feasibility of using 3-channel forehead biosignals (left temporalis, frontalis, and right temporalis channel) as informative channels for emotion recognition during music listening. Classification of four emotional states (positive valence/low arousal, positive valence/high arousal, negative valence/high arousal, and negative valence/low arousal) in arousal–valence space was performed by employing two parallel cascade-forward neural networks as arousal and valence classifiers. The inputs of the classifiers were obtained by applying a fuzzy rough model feature evaluation criterion and sequential forward floating selection algorithm. An averaged classification accuracy of 87.05 % was achieved, corresponding to average valence classification accuracy of 93.66 % and average arousal classification accuracy of 93.29 %.

Classification of Music-Induced Emotions Based on Information Fusion of Forehead Biosignals and Electrocardiogram

Emotion recognition systems have been developed to assess human emotional states during different experiences. In this paper, an approach is proposed for recognizing music-induced emotions through the fusion of three-channel forehead biosignals (the left temporalis, frontalis, and right temporalis channels) and an electrocardiogram. The classification of four emotional states in an arousal–valence space (positive valence/low arousal, positive valence/high arousal, negative valence/high arousal, and negative valence/low arousal) was performed by employing two parallel support vector machines as arousal and valence classifiers. The inputs of the classifiers were obtained by applying a fuzzy-rough model feature evaluation criterion and sequential forward floating selection algorithm. An average classification accuracy of 88.78 % was achieved, corresponding to an average valence classification accuracy of 94.91 % and average arousal classification accuracy of 93.63 %. The proposed emotion recognition system may be useful for interactive multimedia applications or music therapy.

Optimal gene subset selection using the modified SFFS algorithm for tumor classification

A reliable and precise classification of tumors is essential for successful treatment of cancer. Gene selection is an important step for improved diagnostics. The modified SFFS (sequential forward floating selection) algorithm based on weighted Mahalanobis distance, called MSWM, is proposed to identify optimal informative gene subsets taking into account joint discriminatory power for accurate discrimination in this study. Firstly, we make use of the one-dimensional weighted Mahalanobis distance to perform a preliminary selection of genes and then make use of the modified SFFS method and multidimensional weighted Mahalanobis distance to obtain the optimal informative gene subset for tumor classification. Finally, we used the k nearest neighbor and naive Bayes methods to classify tumors based on the optimal gene subset selected using the MSWM method. To validate the efficiency, the proposed MSWM method is applied to classify two different DNA microarray datasets. Our empirical study shows that the MSWM method for tumor classification can obtain better effectiveness of classification than the BWR (the ratio of between-groups to within-groups sum of squares) and IVGA_I (independent variable group analysis I) methods. It suggests that the MSWM gene selection method is ability to obtain correct informative gene subsets taking into account genes’ joint discriminatory power for tumor classification.

An improvement on floating search algorithms for feature subset selection

A new improved forward floating selection (IFFS) algorithm for selecting a subset of features is presented. Our proposed algorithm improves the state-of-the-art sequential forward floating selection algorithm. The improvement is to add an additional search step called “replacing the weak feature” to check whether removing any feature in the currently selected feature subset and adding a new one at each sequential step can improve the current feature subset. Our method provides the optimal or quasi-optimal (close to optimal) solutions for many selected subsets and requires significantly less computational load than optimal feature selection algorithms. Our experimental results for four different databases demonstrate that our algorithm consistently selects better subsets than other suboptimal feature selection algorithms do, especially when the original number of features of the database is large.

Hyperspectral waveband selection for contaminant detection on poultry carcasses

We address the important product inspection application of contaminant detection on chicken carcasses. Detection of four contaminant types of interest (duodenum, ceca, colon, and ingesta) from chickens fed with three different feeds (corn, milo, and wheat) is considered. We consider feature selection algorithms for choosing a small set of spectral bands (wavelengths) in hyperspectral (HS) data for online contaminant detection. For cases when an optimal solution is not realistic, we introduce our new improved forward floating selection algorithm; we call it a quasi-optimal (close to optimal) algorithm. Our algorithm is an improvement on the state-of-the-art sequential forward floating selection algorithm. We train our algorithm on a pixel database using only corn-fed chickens and test it on HS images of carcasses with three feeds. Our new algorithm gives an excellent detection rate and performs better than other suboptimal feature selection algorithms on this database.

Linear feature extraction by integrating pairwise and global discriminatory information via sequential forward floating selection and kernel QR factorization with column pivoting

Linear discriminant analysis (LDA) is often used to produce an effective linear feature extractor for classification. However, some approaches of LDA, such as Fisher's linear discriminant, are not robust to outlier classes. In this paper, a novel approach is proposed to robustly produce an effective linear feature extractor by integrating the discriminatory information from the global and pairwise approaches of LDA. The discriminatory information is integrated either by the sequential forward floating selection algorithm with a criterion function based on the Chernoff bound or by ranking the discriminatory information using the kernel QR factorization with column pivoting according to the indication of an applicability index for these two methods. The proposed approach was compared to various methods of LDA. The experimental results have shown the robustness of the proposed approach and proved the feasibility of the proposed approach.

Feature selection: evaluation, application, and small sample performance

A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward floating selection algorithm, proposed by Pudil et al. (1994), dominates the other algorithms tested. We study the problem of choosing an optimal feature set for land use classification based on SAR satellite images using four different texture models. Pooling features derived from different texture models, followed by a feature selection results in a substantial improvement in the classification accuracy. We also illustrate the dangers of using feature selection in small sample size situations.