P2P Lending Analysis Using the Most Relevant Graph-Based Features

With the rapid development of artificial intelligence in recent years, the research on image processing, text mining, and genome informatics has gradually deepened, and the mining of large-scale databases has begun to receive more and more attention. The objects of data mining have also become more complex, and the data dimensions of mining objects have become higher and higher. Compared with the ultra-high data dimensions, the number of samples available for analysis is too small, resulting in the production of high-dimensional small sample data. High-dimensional small sample data will bring serious dimensional disasters to the mining process. Through feature selection, redundancy and noise features in high-dimensional small sample data can be effectively eliminated, avoiding dimensional disasters and improving the actual efficiency of mining algorithms. However, the existing feature selection methods emphasize the classification or clustering performance of the feature selection results and ignore the stability of the feature selection results, which will lead to unstable feature selection results, and it is difficult to obtain real and understandable features. Based on the traditional feature selection method, this paper proposes an ensemble feature selection method, Random Bits Forest Recursive Clustering Eliminate (RBF-RCE) feature selection method, combined with multiple sets of basic classifiers to carry out parallel learning and screen out the best feature classification results, optimizes the classification performance of traditional feature selection methods, and can also improve the stability of feature selection. Then, this paper analyzes the reasons for the instability of feature selection and introduces a feature selection stability measurement method, the Intersection Measurement (IM), to evaluate whether the feature selection process is stable. The effectiveness of the proposed method is verified by experiments on several groups of high-dimensional small sample data sets.

Facial expression is significant for face-to-face communication since it is one of our body language that increases data information during the communication. In recent surveys, it found that feature extraction methods have influenced on facial recognition directly and they can outperform if some irrelevant features are eliminated. Consequently, a procedure of facial expression recognition using graph-based features and artificial neural network is proposed in this thesis, and the procedure can be divided into 2 phases. For the first phase, fourteen points are manually located to create graph with edges connecting among such points, followed by computation of the Euclidean distances from those edges to define them as features for training in the next phase. For the next phase, Multilayer Perceptrons with back-propagation learning algorithm is implemented to recognize six basic emotions from the corresponding feature vectors. To evaluate the performance, Cohn-Kanade AU-Coded facial expression database is applied to the recognition system under various kinds of feature and training technique, and the experimental results have shown that MLP (Graph-based features) without validation and MLP (Graph-based feature) with cross validation can achieve the highest recognition rate (95.24%). Therefore, it has illustrated that the combination of graph-based features and artificial neural network is the efficient way for the facial expression recognition.

Stability (robustness) of feature selection methods is a topic of recent interest, yet often neglected importance, with direct impact on the reliability of machine learning systems. We investigate the problem of evaluating the stability of feature selection processes yielding subsets of varying size. We introduce several novel feature selection stability measures and adjust some existing measures in a unifying framework that offers broad insight into the stability problem. We study in detail the properties of considered measures and demonstrate on various examples what information about the feature selection process can be gained. We also introduce an alternative approach to feature selection evaluation in the form of measures that enable comparing the similarity of two feature selection processes. These measures enable comparing, e.g., the output of two feature selection methods or two runs of one method with different parameters. The information obtained using the considered stability and similarity measures is shown to be usable for assessing feature selection methods (or criteria) as such.

With the development of technology, large databases become more accessible thanks to automation systems that automatically keep data and allow the use of large databases in many areas. Machine learning approaches, a sub-branch of artificial intelligence, are used in making decisions about the process by analyzing the data stored in databases and converting them into information. In this paper, the body production process of the surgical (medical) mask is analyzed. As it is known, surgical masks have become a part of our lives by becoming widespread all over the world with the COVID-19 pandemic. In the surgical mask body production process, using the real data of the production factors, first of all, filtering feature selection methods and analyzes were made and the feature selection method to be used was determined. With the specified feature selection method, the factors affecting the product quality are determined. Secondly, machine learning methods were used to determine the values and value ranges of factors (features) in the production of defect-free products. The performances of the machine learning models established in the second stage were increased by feature selection analysis. In the study, together with the parameter optimizations made to machine learning algorithms, it was seen that the best algorithm to estimate the defective product rate was the Ibk algorithm with 92.3% accuracy, 91.9% F measurement and 93% AUC value. Finally, in line with the decision rules revealed in the study, it was observed that the fabric types used for the upper/middle/lower layers that make up the body part in the mask body production process greatly affect the rates of defective or defect-free products. If the rod apparatus around the nose belongs to class k, it has been determined that many masks are defective. Improvement suggestions were presented according to the application results.

Classification has been widely studied due to its practical applications. Feature selection aims to improve the classification accuracy by selecting a small feature subset from the original full feature set. However, identification of relevant features is not trivial due to the large search space. Particle swarm optimisation (PSO) is an efficient meta-heuristic algorithm which has shown to be promising in feature selection. However, traditional PSO uses its personal best experience and its historical best experience to determine its search direction, but this learning strategy may limit its performance for feature selection due to the premature convergence. Therefore, the potential of PSO needs to be further explored. In this paper, a new evolutionary learning algorithm termed hybridising PSO with differential evolution (HPSO-DE) is proposed to develop new feature selection methods. In HPSO-DE, differential evolution is applied to breed promising and efficient exemplars for PSO to guide its search, which is expected to not only preserve the diversity of the population but also guide particles to fly to promising areas. HPSO-DE is compared with three classic PSO variants and five traditional feature selection methods on 15 classification problems. The results show that the proposed algorithm can effectively achieve a higher classification accuracy with a smaller feature subset than the compared methods.

The class imbalance problem is encountered in real-world applications of machine learning and results in suboptimal performance during data classification. This is especially true when data is not only imbalanced but also high dimensional. The class imbalance is very often accompanied by a high dimensionality of datasets and in such a case these problems should be considered together. Traditional feature selection methods usually assign the same weighting to samples from different classes when the samples are used to evaluate each feature. Therefore, they do not work good enough with imbalanced data. In situation when the costs of misclassification of different classes are diverse, cost-sensitive learning methods are often applied. These methods are usually used in the classification phase, but we propose to take the cost factors into consideration during the feature selection. In this study we analyse whether the use of cost-sensitive feature selection followed by resampling can give good results for mentioned problems. To evaluate tested methods three imbalanced and multidimensional datasets are considered and the performance of chosen feature selection methods and classifiers are analysed.

Enhanced multi-label feature selection considering label-specific relevant information

Feature selection is a process of representing wanted features based on the requirement needed by selecting the best subset of a dataset without changing the originality of the dataset. The aim of feature selection is to obtain most optimal feature subset to represent the data and for that purpose feature selection offered a few methods. This paper gives an easy understanding of the feature selection concept and the available methods in feature selection. As nowadays metaheuristics is catching attention researchers in many fields and feature selection is one of them, this paper intentionally brief feature selection using metaheuristics that implement Fish Swarm Algorithm (FSA) in the feature selection process. FSA classified as one of the Swarm Intelligence (SI) techniques have several advantages mainly to solve optimization problems. A number of previous works are reviewed. Based on the reviewed and the outcome results that has been tested using high dimensional, real-valued benchmark data sets, FSA reflect good performance among others SI.

CorrCorr: A feature selection method for multivariate correlation network anomaly detection techniques

Subspace distance is an invaluable tool exploited in a wide range of feature selection methods. The power of subspace distance is that it can identify a representative subspace, including a group of features that can efficiently approximate the space of original features. On the other hand, employing intrinsic statistical information of data can play a significant role in a feature selection process. Nevertheless, most of the existing feature selection methods founded on the subspace distance are limited in properly fulfilling this objective. To pursue this void, we propose a framework that takes a subspace distance into account which is called “Variance–Covariance subspace distance”. The approach gains advantages from the correlation of information included in the features of data, thus determines all the feature subsets whose corresponding Variance–Covariance matrix has the minimum norm property. Consequently, a novel, yet efficient unsupervised feature selection framework is introduced based on the Variance–Covariance distance to handle both the dimensionality reduction and subspace learning tasks. The proposed framework has the ability to exclude those features that have the least variance from the original feature set. Moreover, an efficient update algorithm is provided along with its associated convergence analysis to solve the optimization side of the proposed approach. An extensive number of experiments on nine benchmark datasets are also conducted to assess the performance of our method from which the results demonstrate its superiority over a variety of state-of-the-art unsupervised feature selection methods. The source code is available at https://github.com/SaeedKarami/VCSDFS.

Software defect prediction models are used for predicting high risk software components. Feature selection has significant impact on the prediction performance of the software defect prediction models since redundant and unimportant features make the prediction model more difficult to learn. Ensemble feature selection has recently emerged as a new methodology for enhancing feature selection performance. This paper proposes a new multi-criteria-decision-making (MCDM) based ensemble feature selection (EFS) method. This new method is termed as MCDM-EFS. The proposed method, MCDM-EFS, first generates the decision matrix signifying the feature’s importance score with respect to various existing feature selection methods. Next, the decision matrix is used as the input to well-known MCDM method TOPSIS for assigning a final rank to each feature. The proposed approach is validated by an experimental study for predicting software defects using two classifiers K-nearest neighbor (KNN) and naïve bayes (NB) over five open-source datasets. The predictive performance of the proposed approach is compared with existing feature selection algorithms. Two evaluation metrics – nMCC and G-measure are used to compare predictive performance. The experimental results show that the MCDM-EFS significantly improves the predictive performance of software defect prediction models against other feature selection methods in terms of nMCC as well as G-measure.

QSAR analysis of diaryl COX-2 inhibitors: Comparison of feature selection and train-test data selection methods

Feature selection has been widely studied in supervised data mining applications, where the typical goal is to create clusters through the selection of a reduced attribute set that maximizes classification accuracies. Such a goal may not be appropriate for preserving inter-attribute relationships of unlabelled time-series, such as the case of clinical trials data. In this paper, we select the features based on the time-series relationships of attributes by measuring their inter-attribute movement. We present performance measures and methods for feature selection over unlabelled time-series with the aim of preserving inter-attribute relationships. The performance metrics estimate the effectiveness of a given feature set with respect to representation quality by measuring the nearest neighbors before and after feature selection. We provide techniques to combine and compare data from non-standard variable-length time-series sources and provide a mechanism to inject expert opinion into the feature selection process. The methodologies and comparative results are presented in the context of a real pharmaceutical database application.

A label-specific multi-label feature selection algorithm based on the Pareto dominance concept

The paper proposes a hybrid approach to reduce dimension in text classification problems, to overcome the issue of Curse of Dimensionality. This hybrid approach is a combination of Feature Selection (FS) and Feature Extraction (FE) methods, considering different aspects of feature relevance, to effectively reduce the dimension in large text datasets. It prevents feature selection biased in favor of a particular FS method. Many FS methods like Term Variance, Document Frequency, Information Gain, Shannons Entropy measure, Mean-Median and Mean Absolute Difference, were implemented and a comparative study was made on their performance when implemented in a hybrid system. The features selected by the individual FS methods are merged using three approaches, namely, Union, Intersection and Modified Union. The sub lists of features further undergo Feature Extraction by PCA, and the reduced feature sub list is clustered with k-means. Finally, the sentiment-score of the individual clusters are calculated using SentiWordNet database which gives the polarity of the data. The experiments were conducted on the benchmark datasets namely Reuters-21,578 and Classic4. The performance evaluation of the system made using the measures like precision, recall, f-score and accuracy shows that the proposed method has improved performance compared to its competitive methods.