A Novel Outlook on Feature Selection as a Multi-objective Problem

Computer-aided diagnosis has the potential of increasing diagnostic accuracy by providing a second reading to radiologists. In many computerized schemes, numerous features can be extracted to describe suspect image regions. A subset of these features is then employed in a data classifier to determine whether the suspect region is abnormal or normal. Different subsets of features will, in general, result in different classification performances. A feature selection method is often used to determine an "optimal" subset of features to use with a particular classifier. A classifier performance measure (such as the area under the receiver operating characteristic curve) must be incorporated into this feature selection process. With limited datasets, however, there is a distribution in the classifier performance measure for a given classifier and subset of features. In this paper, we investigate the variation in the selected subset of "optimal" features as compared with the true optimal subset of features caused by this distribution of classifier performance. We consider examples in which the probability that the optimal subset of features is selected can be analytically computed. We show the dependence of this probability on the dataset sample size, the total number of features from which to select, the number of features selected, and the performance of the true optimal subset. Once a subset of features has been selected, the parameters of the data classifier must be determined. We show that, with limited datasets and/or a large number of features from which to choose, bias is introduced if the classifier parameters are determined using the same data that were employed to select the "optimal" subset of features.

A new hybrid filter/wrapper algorithm for feature selection in classification

Hyperspectral images (HSIs) generally contain a large amount of spectral bands (features), and the redundant information in them will cause the Hughes phenomenon in the classification process. And feature extraction and feature selection are the two main existing methods to effectively reduce the redundancy of spectral information in the field of HSIs classification. Compared with feature extraction methods, feature selection methods can preserve most of the features of the original HSIs data without losing their valuable details. However, most existing feature selection methods based on single scene (domain) perform poorly in some scenes (domains) with insufficient labeled samples. Therefore, how to adopt an efficient feature selection method to select the optimal feature subsets of source scene and target scene and use the sample information of source scene to assist in the classification of target scene so as to improve the classification accuracy of images in the target scene as much as possible is still very challenging. In order to solve the above problem, this paper proposes a new cross-scene algorithm: Improved Ant Colony Optimization Algorithm-Based Cross-Scene Feature Selection Algorithm (IMACO-CSFS). In order to obtain more accurate feature subsets of the two scenes, IMACO-CSFS proposes a priority sorting-based ant colony strategy to make the subsequent search process focus on the global optimal solution (optimal feature subset) found in the previous iteration. In addition, in order to further accelerate the convergence speed of the global optimal solution, an ant colony strategy based on elite ants is proposed in IMACO-CSFS to more efficiently obtain the optimal feature subsets of the two scenes for training the classifier. Furthermore, this paper simultaneously considers overall classification accuracies of the optimal feature subsets for both scenes and dynamically adjusts their scale to ensure the consistency of the selected features between the two scenes, attenuating the effect of spectral shift and achieving the higher image classification accuracy in the target scene. Experimental results on three cross-scene HSI data pairs demonstrate that IMACO-CSFS is superior in cross-scene feature selection and cross-scene HSIs classification.

In order to obtain the higher classification accuracy in specific categories for the different feature subset, a hierarchical classification algorithm based on Feature Selection is proposed, and is used for synthetic aperture radar (SAR) image classification, and feature selection is achieved by Genetic algorithm. The algorithm has two main characteristics: one is hierarchical classification which consists of many two-class classifier, and the two-class classifier is trained by the optimal feature subset which is selected according to different categories; the second is the classifier of support vector machine SVM (Support Vector Machine); the two is Genetic algorithm which can search out the optimal feature subset and parameters of support vector machine that is most suitable for the category, by unified coding the feature set and the parameters of SVM to constitute the Chromosome. The experiment on the first polarimetric SAR data show that the algorithm can obtain higher classification accuracy rate.

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.

A novel binary greater cane rat algorithm for feature selection

Feature selection (FS) has been studied as a multi-objective problem in recent years to improve the classification effect. In this paper, to expand the ability of Bacterial Foraging Optimization (BFO) in feature selection problems for classification, a Multi-objective Adapting Chemotaxis Bacterial Foraging Optimization (abbreviated as MOACBFO) is proposed. In MOACBFO, a structural variation strategic model is proposed to reduce the computational complexity for multi-objective problems and applied with a dynamically updated external matrix to record the performance of bacteria on two objectives. In addition, an adaptive chemotaxis step mechanism is designed to help the bacteria jump out of the local optimality. To further enhance the diversity of bacteria searching capability, a feature subset updating strategy is developed. The optimal feature subset and fitness value are stored in the external matrix continuously by comparing them with the historical value records. The performance of the proposed algorithm is demonstrated by comparing it with four other advanced swarm intelligent algorithms on 11 high-dimensional microarray datasets. The results indicate that the MOACBFO performs better in achieving a lower classification error rate using a small number of features.

In real world, data describing the same learning task may be distributed in different institutions (called participants), and these participants cannot share their own data due to the need of privacy protection. How to select an optimal feature subset on the basis of ensuring the data privacy of each participant is an important and challenging topic. At the same time, data on part participants show imbalanced or even miss some classes, further increasing the difficulty of solving this kind of problem. In view of this, by introducing a trusted third party to process and integrate optimal information from participants, this paper proposes a multi-participant federated evolutionary feature selection algorithm for imbalanced data under privacy protection. Firstly, a multi-level joint sample filling strategy with multiple participants (called sampling-rough selection-fine tuning strategy) is proposed to fill imbalanced or empty classes on each participant while ensuring data privacy. Then, a federated evolutionary feature selection method based on particle swarm optimization (PSO) is proposed by periodically sharing the optimal feature subsets obtained by PSOs on participants. Finally, the proposed algorithm is applied to 15 test datasets, and compared with several typical imbalanced feature selection algorithms and two kinds of ensemble feature selection algorithms. The experimental results show that the proposed algorithm can significantly promote the ability of each participant on processing imbalanced datasets, and improve the classification accuracy of obtained feature subsets while protecting the participants' data privacy.

Feature selection has become a de facto tool for analyzing high-dimensional data, especially in bioinformatics. It is effective in improving learning algorithms’ scalability and facilitating feature generalization or interpretability by removing noise and redundancy. Our focus is placed on the paradigm of supervised feature selection, which aims to find an optimal feature subset to best predict the target. We propose a nonlinear approach for finding a feature subset that achieves the highest inter-class separability in terms of the kernel Discriminant Information (KDI) measure. Theoretically, we prove the existence of good prediction hypotheses for feature subsets with high KDI value. We also establish the equivalency between maximizing the KDI statistic and minimizing a functional dependency measure of label variable on data. Moreover, we asymptotically prove the concentration property of the optimal feature subset found by maximizing the KDI measure. Practically, we provide an efficient gradient optimization algorithm for solving the KDI feature selection problem. We evaluate the proposed method based on 19 benchmark datasets in various domains, and demonstrates a noticeable improvement against state-of-the-art baselines on the majority of classification and regression tasks. Notably, our method is robust to the choice of hyper-parameters, works well with various downstream classifiers, has competitive computational complexity among the kernel based methods considered, and scales well the large-scale object recognition dataset, with generalization enhancement on CIFAR.

Feature selection entails identifying a subset of the most useful features that produces compatible results as the original entire set of features. A feature selection algorithm can be assessed in terms of both efficiency and effectiveness. While efficiency is concerned with the time required to find a subset of features, effectiveness is concerned with the quality of the subset of features. This paper proposes and experimentally evaluates a fast clustering-based feature selection algorithm, FAST, based on these criteria.  The FAST algorithm operates in two steps. Graph-theoretic clustering methods are used to partition characteristics into clusters in the initial stage. The most representative feature from each cluster that is strongly related to target classes is chosen in the second stage to construct a subset of features. Because the properties in various clusters are relatively independent, FAST's clustering-based technique is likely to produce a subset of valuable and independent features. We use the efficient Minimum-spanning tree clustering method to ensure FAST's efficiency. An empirical study is conducted to assess the efficiency and effectiveness of the FAST algorithm. FAST and several representative feature selection algorithms, such as FCBF, ReliefF, CFS, Consist, and FOCUS-SF, are compared to four types of well-known classifiers, including the probability-based Naive Bayes, the tree-based C4.5, the instance-based IB1, and the rule-based RIPPER, before and after feature selection. FAST not only provides smaller subsets of features but also improves the performances of the four types of classifiers, according to the findings, which were based on 35 publicly accessible real-world high-dimensional image, microarray, and text data.

The intelligent analysis of Databases may be affected by the presence of unimportant features, which motivates the application of Feature Selection. By treating this task as a search and optimization process, it is possible to use the synergy between Genetic Algorithms and Multi-objective Optimization to carry out the search for (quasi) optimal subsets of features considering possible conflicting importance criteria. This work presents an application of Multi-objective Genetic Algorithms to the Feature Selection problem, combining different criteria measuring the importance of the subsets of features.

Large-scale feature selection is one of the most important fields in the big data domain that can solve real data problems, such as bioinformatics, where it is necessary to process huge amount of data. The efficiency of existing feature selection algorithms significantly downgrades, if not totally inapplicable, when data size exceeds hundreds of gigabytes, because most feature selection algorithms are designed for centralized computing architecture. For that, distributed computing techniques, such as MapReduce can be applied to handle very large data. Our approach is to scale the existing method for feature selection, Kmeans clustering and Signal to Noise Ratio (SNR) combined with optimization technique as Binary Particle Swarm Optimization (BPSO). The proposed method is divided into two stages. In the first stage, we have used parallel Kmeans on MapReduce for clustering features, and then we have applied iterative MapReduce that implement parallel SNR ranking for each cluster. After, we have selected the top ranked feature from each cluster. The top scored features from each cluster are gathered and a new feature subset is generated. In the second stage, the new feature subset is used as input to the proposed BPSO based on MapReduce which provides an optimized feature subset. The proposed method is implemented in a distributed environment, and its efficiency is illustrated through analyzing practical problems such as biomarker discovery.

Chapter 14 - Review on hybrid feature selection and classification of microarray gene expression data

In the field of hand gesture recognition based on electromyography, the selection of features is still a matter of research because the accuracy of feature evaluation techniques depends on the distribution underling a feature space. Currently, there are a lot of kinds of features, so a selection method is required in order to find an optimal feature subset. The selection of features can be done based on either classifier methods or statistical measurement indexes. On one hand, the use of classifier methods takes considerable time, consequently, they are computationally expensive. On the other hand, the use of a statistical index allows to select features based on properties, and they are less expensive in terms of time and computational costs. In this context, this study analyzed different statistical indexes to discover optimal feature subsets and predicts which one may have the best performance in terms of classification and recognition accuracy. For this purpose, four indexes were analyzed: the sum of RES (separation and compactness index), the sum of Fuzzy Entropies, and both of these indexes divided by the mutual information of feature subset. These index values were calculated for 10 different feature subsets obtained from nineteen time-domain features which have been investigated by many researchers for extracting useful information from an electromyography signal. The feature subsets were implemented in four classifiers (Artificial Neural Network, k-nearest Neighbors Algorithm, Support Vector Machine, and decision trees) to find the best statistical index which tries to quantify the suitableness of the feature space. Each tested index in this work was analyzed in terms of classification and recognition accuracy for each machine learning algorithm.KeywordsHand gesture recognitionElectromyographyFeature evaluationFuzzy entropyMutual informationMyo armband sensor

The goal of feature selection is to search the optimal feature subset with respect to the evaluation function. Exhaustively searching all possible feature subsets requires high computational cost. The alternative suboptimal methods are more efficient and practical but they cannot promise globally optimal results. We propose a new feature selection algorithm based on distance discriminant and distribution overlapping (HFSDD) for continuous features, which overcomes the drawbacks of the exhaustive search approaches and those of the suboptimal methods. The proposed method is able to find the optimal feature subset without exhaustive search or Branch and Bound algorithm. The most difficult problem for optimal feature selection, the search problem, is converted into a feature ranking problem following rigorous theoretical proof such that the computational complexity can be greatly reduced. Since the distribution of overlapping degrees between every two classes can provide useful information for feature selection, HFSDD also takes them into account by using a new approach to estimate the overlapping degrees. In this sense, HFSDD is a distance discriminant and distribution overlapping based solution. HFSDD was compared with ReliefF and mrmrMID on ten data sets. The experimental results show that HFSDD outperforms the other methods.