Joint Semi-Supervised Feature Selection and Classification through Bayesian Approach

When the number of labeled examples is limited, traditional supervised feature selection techniques often fail due to sample selection bias or unrepresentative sample problem. To solve this, semi-supervised feature selection techniques exploit the statistical information of both labeled and unlabeled examples in the same time. However, the results of semi-supervised feature selection can be at times unsatisfactory, and the culprit is on how to effectively use the unlabeled data. Quite different from both supervised and semi-supervised feature selection, we propose a ldquohybridrdquoframework based on graph models. We first apply supervised methods to select a small set of most critical features from the labeled data. Importantly, these initial features might otherwise be missed when selection is performed on the labeled and unlabeled examples simultaneously. Next,this initial feature set is expanded and corrected with the use of unlabeled data. We formally analyze why the expected performance of the hybrid framework is better than both supervised and semi-supervised feature selection. Experimental results demonstrate that the proposed method outperforms both traditional supervised and state-of-the-art semi-supervised feature selection algorithms by at least 10% inaccuracy on a number of text and biomedical problems with thousands of features to choose from. Software and dataset is available from the authors.

Semi-supervised feature selection for partially labeled mixed-type data based on multi-criteria measure approach

Semi-supervised multi-label feature selection with adaptive structure learning and manifold learning

In this paper, we propose a semi-supervised margin-based feature selection algorithm called Relief-Sc. It is a modification of the well-known Relief algorithm from its optimization perspective. It utilizes cannot-link constraints only to solve a simple convex problem in a closed form giving a unique solution. Experimental results on well-known datasets validate the effectiveness of our proposed algorithm. Only with little supervision information, Relief-Sc proved to be comparable to supervised feature selection algorithms and was superior to the unsupervised ones.

A graph Laplacian based approach to semi-supervised feature selection for regression problems

With the growth of Internet, there has been a tremendous increases in the number of attacks and therefore Intrusion Detection Systems (IDS’s) has become a main stream of information security. The purpose of IDS is to help the computer systems to deal with attacks. This anomaly detection system creates a database of normal behaviour and deviations from the normal behaviour to trigger during the occurrence of intrusions. Based on the source of data, IDS is classified into Host based IDS and Network based IDS. In network based IDS, the individual packets flowing through the network are analyzed where as in host based IDS the activities on the single computer or host are analyzed. The feature selection used in IDS helps to reduce the classification time. In this paper, the IDS for detecting the attacks effectively has been proposed and implemented. For this purpose, a new feature selection algorithm called Optimal Feature Selection algorithm based on Information Gain Ratio has been proposed and implemented. This feature selection algorithm selects optimal number of features from KDD Cup dataset. In addition, two classification techniques namely Support Vector Machine and Rule Based Classification have been used for effective classification of the data set. This system is very efficient in detecting DoS attacks and effectively reduces the false alarm rate. The proposed feature selection and classification algorithms enhance the performance of the IDS in detecting the attacks.

In the present state of affairs, the motive behind every educational organization is to uplift the academic achievement of students. Educational data mining (EDM) is an upward field of research, and it is very helpful for academic institutions to predict the academic performance of the students. Educational datasets are the basis of various predictive models. Quality of these models can be improved by using feature selection (FS). To get the required benefits from the available data, there must be some tools for analysis and prediction. In lieu of the above, machine learning/data mining are most suitable. In educational data mining, for better accuracy of prediction models’ and quality of various educational datasets, feature selection (FS) plays a vital role. Feature selection (FS) algorithms abolish inappropriate information from the repositories of educational background so that performance of classifier in terms of accuracy could be increased and the same could be used for better decision. In lieu of the above, a best feature selection algorithm must be selected. In this paper, two filter selection approaches namely correlation feature selection (CFS) and wrapper-based feature selection have been used to demonstrate the importance of selection of a feature subset for a classification problem. The present paper aims to find the detailed investigation of filter feature selection algorithms along with the classification algorithms on a given dataset. We found result with numerous numbers of features from various Feature selection algorithms and classifiers which will help the researcher to discover the most excellent mixture of filter feature selection algorithms and its associated classifiers. The result indicates that SMO and J48 have the highest accuracy measures with the correlation feature selection algorithms, while Naïve Bayes has the highest accuracy measures with the wrapper subset feature selection algorithms for predicting high, medium and low grade for the students.

Based on benchmarking various methods and analyzing multiple real scRNA-seq datasets, a computational platform/workflow and a set of tips for best practices are developed for analyzing causal interactions or relationships in single cells.

In recent past, the features selection plays a significant role in machine learning algorithms to improve their performance in terms of increasing the classification accuracy and reducing the classifier building time. Therefore, it is essential to evaluate the performance of the features selection method that is proposed. Moreover, the clustering algorithm also be used some cases in the feature selection process for removing the redundant features from the high-dimensional space therefore identifying the suitable clustering algorithm also be important. Hence, this paper presents the performance metrics for feature selection, clustering and classification algorithms.

Neurodynamics-driven holistic approaches to semi-supervised feature selection

This paper investigates the characteristics of a clinical dataset using a combination of feature selection and classification methods to handle missing values and understand the underlying statistical characteristics of a typical clinical dataset. Typically, when a large clinical dataset is presented, it consists of challenges such as missing values, high dimensionality, and unbalanced classes. These pose an inherent problem when implementing feature selection and classification algorithms. With most clinical datasets, an initial exploration of the dataset is carried out, and those attributes with more than a certain percentage of missing values are eliminated from the dataset. Later, with the help of missing value imputation, feature selection and classification algorithms, prognostic and diagnostic models are developed. This paper has two main conclusions: 1) Despite the nature of clinical datasets, and their large size, methods for missing value imputation do not affect the final performance. What is crucial is that the dataset is an accurate representation of the clinical problem and those methods of imputing missing values are not critical for developing classifiers and prognostic/diagnostic models. 2) Supervised learning has proven to be more suitable for mining clinical data than unsupervised methods. It is also shown that non-parametric classifiers such as decision trees give better results when compared to parametric classifiers such as radial basis function networks (RBFNs).

New hybrid data mining model for credit scoring based on feature selection algorithm and ensemble classifiers

With the continuous development of Internet technology, data gradually present a complicated and high-dimensional trend. These high-dimensional data have a large number of redundant features and irrelevant features, which bring great challenges to the existing machine learning algorithms. Feature selection is one of the important research topics in the fields of machine learning, pattern recognition and data mining, and it is also an important means in the data preprocessing stage. Feature selection is to look for the optimal feature subset from the original feature set, which would improve the classification accuracy and reduce the machine learning time. The traditional feature selection algorithm tends to ignore the kind of feature which has a weak distinguishing capacity as a monomer, whereas the feature group’s distinguishing capacity is strong. Therefore, a new dynamic interaction feature selection (DIFS) algorithm is proposed in this paper. Initially, under the theoretical framework of interactive information, it redefines the relevance, irrelevance and redundancy of the features. Secondly, it offers the computational formulas for calculating interactive information. Finally, under the eleven data sets of UCI and three different classifiers, namely, KNN, SVM and C4.5, the DIFS algorithm increases the classification accuracy of the FullSet by 3.2848% and averagely decreases the number of features selected by 15.137. Hence, the DIFS algorithm can not only identify the relevance feature effectively, but also identify the irrelevant and redundant features. Moreover, it can effectively improve the classification accuracy of the data sets and reduce the feature dimensions of the data sets.

Irrelevant feature in heart disease dataset affects the performance of binary classification model. Consequently, eliminating irrelevant and redundant feature (s) from training set with feature selection algorithm significantly improves the performance of classification model on heart disease detection. Sequential feature selection (SFS) is successful algorithm to improve the performance of classification model on heart disease detection and reduces the computational time complexity. In this study, sequential feature selection (SFS) algorithm is implemented for improving the classifier performance on heart disease detection by removing irrelevant features and training a model on optimal features. Furthermore, exhaustive and permutation based feature selection algorithm are implemented and compared with SFS algorithm. The implemented and existing feature selection algorithms are evaluated using real world Pima Indian heart disease dataset and result appears to prove that the SFS algorithm outperforms as compared to exhaustive and permutation based feature selection algorithm. Overall, the result looks promising and more effective heart disease detection model is developed with accuracy of 99.3%.

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.