Enhanced Category-Feature Association Measure

In text classification, taking words in text documents as features creates a very high dimensional feature space. This is known as the high dimensionality problem in text classification. The most common and effective way to solve this problem is to select an ideal subset of features using a feature selection approach. In this paper, a new feature selection approach called Rough Information Gain (RIG) is presented as a solution to the high dimensionality problem. Rough Information Gain extracts hidden and meaningful patterns in text data with the help of Rough Sets and computes a score value based on these patterns. The proposed approach utilizes the selection strategy of the Information Gain Selection (IG) approach when pattern extraction is completely uncertain. To demonstrate the performance of the Rough Information Gain in the experimental studies, the Micro-F1 success metric is used to compare with Information Gain Selection (IG), Chi-Square (CHI2), Gini Coefficient (GI), Discriminative Feature Selector (DFS) approaches. The proposed Rough Information Gain approach outperforms the other methods in terms of performance, according to the results.

Day by day the number of text documents in digital form is increasing. Text classification is used to organize these text documents. However, text classification has the problem of high dimensionality of feature space. This high dimensionality of feature space is solved by feature selection and feature extraction methods and improves the performance of text categorization. The feature selection and feature extraction techniques remove the irrelevant features from the text documents and reduce the dimensionality of feature space. This paper presents the various feature selection and feature extraction methods. This paper also discusses various classifiers to classify the documents.

The trend of current brain computer interfaces (BCI) seek to establish bi-directional communication with the brain, for instance, recovering motor functions by externally controlling devices and directly stimulating the brain. This will greatly assist paralyzed individuals through bypassing the damaged brain region. The key process of this communication interface is to decode movements from neural signals and encode information into neural activity. The majority of decoding or pattern classification studies have focused on cortical areas for BCIs, but deep brain structures have also been involved in motor control. The subthalamic nucleus (STN) in the basal ganglia is involved in the preparation, execution and imagining of movements, and may be an alternative source for driving BCIs. This study therefore aimed to classify patterns of deep brain local field potentials (LFPs) related to execution of visually cued movements. LFPs were recorded bilaterally from the STN through deep brain stimulation electrodes implanted in patients with Parkinson's disease. The frequency dependent components of the LFPs were extracted using the wavelet packet transform. In each frequency component, signal features were extracted using an alternative approach called neural synchronization by analyzing Granger causality between the STN. Based on these extracted features, a new feature selection strategy, namely weighted sequential feature selection (WSFS) was developed to efficiently select the optimal feature subset. A support vector machine (SVM) classifier was implemented alongside this novel feature extraction and selection strategy, and evaluated using a cross-validation procedure. Using this optimised feature subset, average correct pattern classification accuracy of movement (left or right) reached 76.0±3.1%. The results obtained in this study are encouraging and suggest that the neural activity in the deep neural circuit (basal ganglia) can be used for controlling BCIs.

High dimension and complexity of network high-dimensional data lead to poor feature selection effect network high-dimensional data. To effectively solve this problem, feature selection algorithms for high-dimensional network data based on supervised discriminant projection (SDP) have been designed. The sparse representation problem of high-dimensional network data is transformed into an Lp norm optimization problem, and the sparse subspace clustering method is used to cluster high-dimensional network data. Dimensionless processing is carried out for the clustering processing results. Based on the linear projection matrix and the best transformation matrix, the dimensionless processing results are reduced by combining the SDP. The sparse constraint method is used to achieve feature selection of high-dimensional data in the network, and the relevant feature selection results are obtained. The experimental findings demonstrate that the suggested algorithm can effectively cluster seven different types of data and converges when the number of iterations approaches 24. The F1 value, recall, and precision are all kept at high levels. High-dimensional network data feature selection accuracy on average is 96.9%, and feature selection time on average is 65.1 milliseconds. The selection effect for network high-dimensional data features is good.

As two major parts for tackling high-dimensional cancer microarray gene data sets, feature selection and classification have attracted an increasing interest in academia and medical community. Since cancer gene expression data sets have small samples, high dimensionality, and class imbalance problems, extracting useful gene information and effective classification becomes more challenging. In this paper, we propose a novel feature selection algorithm called ISVM-RFE(FPD) for classification, which fully utilizes classification performance of each feature subset. Compared to the existing algorithms, ISVM-RFE(FPD) takes into account not only the intrinsic characteristic of the data, but also both linear and nonlinear correlation among features. The experimental results demonstrate that ISVM-RFE(FPD) outperforms the existing SVM-based feature selection algorithms in terms of recall rate of positive samples (rr p ) and G-mean (G).

In multi-label text classification, the central aim is to associate an array of descriptive labels for a better understanding of the text. There are three main challenges in doing multi-label text classification (i) a large number of text (input) features, (ii) the underlying implicit relationship between input features and output labels, and (iii) an implicit inter-label dependency. In traditional approaches to multi-label classification, these problems are not being addressed collectively. A feature selection strategy that inherently uses local features to discriminate a class and similarly global features that can distinctly separate classes can be very effective for multi-label classification. In this research, we perform a feature selection and ranking strategy based on local and global features. A Naïve Bayes classifier is being used using a combination of these two -feature sets, it is compared with the baseline implemented with the term frequency-inverse document frequency (TF-IDF). A series of experiments have been carried out on standard multi-label text datasets, using evaluation metrics like Hamming loss, Subset Accuracy and Micro/Macro F1 scores, and encouraging results are obtained.

Background Machine learning (ML) algorithm support vector machine (SVM) performed better than Thrombolysis in Myocardial Infarction (TIMI) score for ASIAN STEMI patients. However, Deep Learning (DL) effectiveness in the multiethnic ASIAN population has yet to be determined. DL has automatic learning of the feature from a given dataset without the need to conduct feature selection. However, the selected features by the algorithm is black box. Identifying features associated with mortality is essential to recognize characteristics of patients with high risk for better patient management. Purpose To develop a DL algorithm for in-hospital mortality in multiethnic STEMI patients using predictors identified from the SVM algorithm. To investigate DL performance constructed using predictors from SVM feature extraction and expert-recommended predictors. Methods We constructed four algorithms; a) DL and SVM algorithms with predictors identified from the SVM variable importance b) DL and SVM using predictors based on expert recommendation. We used registry data from the National Cardiovascular Disease Database of 11397 patient's. Fifty parameters including demographics, cardiovascular risk, medications and clinical variables were considered. The Area under the curve (AUC) is the performance evaluation metric. Algorithms were validated against the TIMI and tested using the same validation data. SVM variable importance with backward elimination was used to select and rank important variables. Results DL algorithms outperform SVM and TIMI on the validation dataset; i) DL with SVM selected predictors (15 predictors, AUC = 0.97), ii) DL with expert-recommended predictors (16 predictors, AUC = 0.96), iii) SVM with selected predictors (15 predictors, AUC = 0.92), iv) SVM with expert-recommended predictors (AUC = 0.89) and TIMI (AUC = 0.82). Common predictors across SVM feature selection, expert-recommendation and TIMI are: age, heart rate, Killip class, fasting blood glucose, systolic blood pressure, comorbid diseases and ST-elevation. SVM feature selection also identified diuretics, PCI and pharmacotherapy drugs as predictors that improve mortality prediction in STEMI patients. Our findings suggest that the TIMI score underestimates patients risk of mortality. DL algorithm using selected predictors classified 35% of nonsurvival patients as high risk (risk probabilities >50%) compared to only 12.7% nonsurvival patients by TIMI (score >5) (Figure below). Conclusions In the ASIAN population, patients with STEMI can be better classified using the DL algorithm compared to the ML and TIMI score. Combining ML feature selection with DL allows the identification of distinct factors in a unique ASIAN population for better mortality prediction than relying solely on an expert recommendation as it is a very subjective approach. Continuous validation on population-specific algorithms using DL and ML is needed before implementing in a real clinical setting. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1 TIMI performance on validation setDL performance on validation set

BackgroundMachine learning (ML) algorithm support vector machine (SVM) performed better than Thrombolysis in Myocardial Infarction (TIMI) score for ASIAN STEMI patients. However, Deep Learning (DL) effectiveness in the multiethnic ASIAN population has yet to be determined. DL has automatic learning of the feature from a given dataset without the need to conduct feature selection. However, the selected features by the algorithm is black box. Identifying features associated with mortality is essential to recognize characteristics of patients with high risk for better patient management.PurposeTo develop a DL algorithm for in-hospital mortality in multiethnic STEMI patients using predictors identified from the SVM algorithm. To investigate DL performance constructed using predictors from SVM feature extraction and expert-recommended predictors.MethodsWe constructed four algorithms; a) DL and SVM algorithms with predictors identified from the SVM variable importance b) DL and SVM using predictors based on expert recommendation. We used registry data from the National Cardiovascular Disease Database of 11397 patient's. Fifty parameters including demographics, cardiovascular risk, medications and clinical variables were considered. The Area under the curve (AUC) is the performance evaluation metric. Algorithms were validated against the TIMI and tested using the same validation data. SVM variable importance with backward elimination was used to select and rank important variables.ResultsDL algorithms outperform SVM and TIMI on the validation dataset; i) DL with SVM selected predictors (15 predictors, AUC = 0.97), ii) DL with expert-recommended predictors (16 predictors, AUC = 0.96), iii) SVM with selected predictors (15 predictors, AUC = 0.92), iv) SVM with expert-recommended predictors (AUC = 0.89) and TIMI (AUC = 0.82). Common predictors across SVM feature selection, expert-recommendation and TIMI are: age, heart rate, Killip class, fasting blood glucose, systolic blood pressure, comorbid diseases and ST-elevation. SVM feature selection also identified diuretics, PCI and pharmacotherapy drugs as predictors that improve mortality prediction in STEMI patients. Our findings suggest that the TIMI score underestimates patients risk of mortality. DL algorithm using selected predictors classified 35% of nonsurvival patients as high risk (risk probabilities >50%) compared to only 12.7% nonsurvival patients by TIMI (score >5) (Figure below).ConclusionsIn the ASIAN population, patients with STEMI can be better classified using the DL algorithm compared to the ML and TIMI score. Combining ML feature selection with DL allows the identification of distinct factors in a unique ASIAN population for better mortality prediction than relying solely on an expert recommendation as it is a very subjective approach. Continuous validation on population-specific algorithms using DL and ML is needed before implementing in a real clinical setting.Funding AcknowledgementType of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1TIMI performance on validation setDL performance on validation set

A major characteristic of text document classification problem is extremely high dimensionality of text data. In this paper we present two algorithms for feature (word) selection for the purpose of text classification. We used sequential forward selection methods based on improved mutual information introduced by Battiti [1] and Kwak and Choi [6] for non-textual data. These feature evaluation functions take into consideration how features work together. The performance of these evaluation functions compared to the information gain which evaluate features individually is discussed. We present experimental results using naive Bayes classifier based on multinomial model on the Reuters data set. Finally, we analyze the experimental results from various perspectives, including F 1-measure, precision and recall. Preliminary experimental results indicate the effectiveness of the proposed feature selection algorithms in a text classification problem.

Distributed feature selection (DFS) strategy for microarray gene expression data to improve the classification performance

Data-driven market segmentation is a popular and widely used segmentation method in tourism. It aims to identify market segments among tourists who are similar to each other, thus allowing a targeted marketing mix to be developed. Typically data used to segment tourists are characterized by small numbers of respondents and large numbers of survey questions. Small samples and numerous questions cause serious methodological problems that have typically been addressed by using factor-cluster analysis to reduce the dimensionality of data. Recently, factor-cluster analysis has been shown as an unacceptable solution to the problem of high data dimensionality in segmentation. In this article, the authors introduce biclustering, a novel approach to address the problem of high dimensionality in tourism segmentation studies. We discuss the circumstances in which biclustering should be used rather than parametric or nonparametric grouping techniques. An illustrative example of how biclustering is computed is also provided.

We consider feature selection for text classification both theoretically and empirically. Our main result is an unsupervised feature selection strategy for which we give worst-case theoretical guarantees on the generalization power of the resultant classification function f with respect to the classification function f obtained when keeping all the features. To the best of our knowledge, this is the first feature selection method with such guarantees. In addition, the analysis leads to insights as to when and why this feature selection strategy will perform well in practice. We then use the TechTC-100, 20-Newsgroups, and Reuters-RCV2 data sets to evaluate empirically the performance of this and two simpler but related feature selection strategies against two commonly-used strategies. Our empirical evaluation shows that the strategy with provable performance guarantees performs well in comparison with other commonly-used feature selection strategies. In addition, it performs better on certain datasets under very aggressive feature selection.

In high-dimensional data, penalized regression is often used for variable selection and parameter estimation. However, these methods typically require time-consuming cross-validation methods to select tuning parameters and retain more false positives under high dimensionality. This chapter discusses sparse boosting based machine learning methods in the following high-dimensional problems. First, a sparse boosting method to select important biomarkers is studied for the right censored survival data with high-dimensional biomarkers. Then, a two-step sparse boosting method to carry out the variable selection and the model-based prediction is studied for the high-dimensional longitudinal observations measured repeatedly over time. Finally, a multi-step sparse boosting method to identify patient subgroups that exhibit different treatment effects is studied for the high-dimensional dense longitudinal observations. This chapter intends to solve the problem of how to improve the accuracy and calculation speed of variable selection and parameter estimation in high-dimensional data. It aims to expand the application scope of sparse boosting and develop new methods of high-dimensional survival analysis, longitudinal data analysis, and subgroup analysis, which has great application prospects.

Gaussian process (GP) regression or kriging has been extensively applied in the engineering literature for the purposes of building a cheap-to-evaluate surrogate, within the contexts of multi-fidelity modeling, model calibration, and design optimization. With the ongoing automation of manufacturing and industrial practices as a part of Industry 4.0, there has been a greater need for advancing GP regression techniques to handle challenges such as high input dimensionality, data paucity or big data problems, these consist primarily of proposing efficient design of experiments, optimal data acquisition strategies, sparsifying covariance kernels, and other mathematical tricks. In this work, our attention is focused on the challenges of efficiently training a GP model, which, to the authors opinion, has attracted very little attention and is to-date poorly addressed. The performance of widely used training approaches such as maximum likelihood estimation and Markov Chain Monte Carlo (MCMC) sampling can deteriorate significantly in high-dimensional and big data problems and can lead to cost deficient implementations of critical importance to many industrial applications. Here, we compare an Adaptive Sequential Monte Carlo (ASMC) sampling algorithm to classic MCMC sampling strategies and we demonstrate the effectiveness of our implementation on several mathematical problems and challenging industry applications of varying complexity. The computational time savings of the ASMC approach manifest in large-scale problems helping us to push the boundaries of applicability and scalability of GPs for model calibration in various domains of the industry, including but not limited to design automation, design engineering, smart manufacturing, predictive maintenance, and supply chain manufacturing.

Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection