Similarity-based Feature Selection Research Articles

Poverty Level Prediction Based on E-Commerce Data Using Naïve Bayes Algorithm and Similarity-Based Feature Selection

The poverty rate is an important measure of any country because it indicates how well the economy develops and how well the economic prosperity distributes among citizens. The Central Statistics Agency, or BPS, measures the poverty rates in Indonesia using the concept of the ability to meet demands (basic needs approach). Using this approach, spending becomes a measure of poverty, defined as an economic incapacity to satisfy food and non-food requirements. Thus, the poor are individuals whose monthly per capita spending is less than the poverty threshold. In this study, the machine learning method using Naive Bayes with similarity-based feature selection and e-commerce data has been proposed to predict the poverty level in Indonesia. We proposed the method to be used as a complement to the results of the costly surveys and censuses conducted by BPS. Our experiments show that the classifier shows little relevance between the predicted and the original values or actual poverty prediction based on BPS data. A limited number of features does not necessarily result in poor accuracy, however great accuracy is not always achieved if a lot of features are being used.

Fault Classification of Nonlinear Small Sample Data through Feature Sub-Space Neighbor Vote

The fault classification of a small sample of high dimension is challenging, especially for a nonlinear and non-Gaussian manufacturing process. In this paper, a similarity-based feature selection and sub-space neighbor vote method is proposed to solve this problem. To capture the dynamics, nonlinearity, and non-Gaussianity in the irregular time series data, high order spectral features, and fractal dimension features are extracted, selected, and stacked in a regular matrix. To address the problem of a small sample, all labeled fault data are used for similarity decisions for a specific fault type. The distances between the new data and all fault types are calculated in their feature subspaces. The new data are classified to the nearest fault type by majority probability voting of the distances. Meanwhile, the selected features, from respective measured variables, indicate the cause of the fault. The proposed method is evaluated on a publicly available benchmark of a real semiconductor etching dataset. It is demonstrated that by using the high order spectral features and fractal dimensionality features, the proposed method can achieve more than 84% fault recognition accuracy. The resulting feature subspace can be used to match any new fault data to the fingerprint feature subspace of each fault type, and hence can pinpoint the root cause of a fault in a manufacturing process.

TB-CoAuth: Text based continuous authentication for detecting compromised accounts in social networks

Commencement of research towards compromised account detection in email and web services foreshadows the growth of the same in social network scenario. In this paper, continuous authentication of textual content has been performed for incessant authorship verification to detect compromised accounts in social networks. Four categories of features namely, content free, content specific, stylometric and folksonomy are extracted and evaluated. Experiments are performed with 3057 twitter users taking 4000 latest tweets for each user. It is evident from the experiments that consistency maintained on features is different for each user. Hence, various statistical and similarity-based feature selection techniques are used to rank and select optimal features for each user which are further combined using a popular rank aggregation technique called BORDA. Also, performance of various supervised machine learning classifiers is analyzed on the basis of different evaluation metrics. Experimental results state that for the undertaken problem, SVM with rbf kernel outperformed other classifiers namely, kNN, Random Forest, Gradient Boosted and Multi Layer Perceptron, attaining a maximum F-score of 94.57% under the varied parameter settings.

Semantic analysis-based relevant data retrieval model using feature selection, summarization and CNN

Semantic analysis is playing a major role and task in text mining process caused by the presence of huge number of relevant and irrelevant data in Internet and other resources. Here, the semantic-based text summarization must be incorporated for the successful relevant data extraction by using data classification. The accurate classification process is done by using deep learning techniques recently. However, no existing model is achieved reasonable relevancy accuracy. For overcoming the drawbacks, we propose an effective semantic analysis-based relevant data retrieval model for retrieving the relevant data from local repository or web applications in Internet. This new model consists of (i) semantic similarity-based feature selection and (ii) enrichment technique, (iii) data summarization technique and iv) text relationship-based deep neural network classifier. Here, we propose a new semantic analysis-based feature selection algorithm to select the similarity indexed relevant data from local repositories or web applications. In addition, a new semantic-based data summarization technique is also introduced for summarizing the text that is available in the online resources. Finally, a new semantic similarity-based deep neural network-based classifier is also introduced for categorizing the data according to the semantic relation. The proposed model is proved the effectiveness of the data retrieval process by conducting various experiments based on the relevant data extraction from Internet resources, and it also tested with the recognized datasets.

Feature Selection Through Message Passing.

A novel similarity-based feature selection algorithm is developed, using the concept of distance correlation. A feature subset is selected in terms of this similarity measure between pairs of features, without assuming any underlying distribution of the data. The pair-wise similarity is then employed, in a message passing framework, to select a set of exemplars features involving minimum redundancy and reduced parameter tuning. The algorithm does not need an exhaustive traversal of the search space. The methodology is next extended to handle large data, using an inherent property of distance correlation. The effectiveness of the algorithm is demonstrated on nine sets of publicly-available data.

Intelligent facial emotion recognition using a layered encoding cascade optimization model

In this research, we propose a facial expression recognition system with a layered encoding cascade optimization model. Since generating an effective facial representation is a vital step to the success of facial emotion recognition, a modified Local Gabor Binary Pattern operator is first employed to derive a refined initial face representation and we then propose two evolutionary algorithms for feature optimization including (i) direct similarity and (ii) Pareto-based feature selection, under the layered cascade model. The direct similarity feature selection considers characteristics within the same emotion category that give the minimum within-class variation while the Pareto-based feature optimization focuses on features that best represent each expression category and at the same time provide the most distinctions to other expressions. Both a neural network and an ensemble classifier with weighted majority vote are implemented for the recognition of seven expressions based on the selected optimized features. The ensemble model also automatically updates itself with the most recent concepts in the data. Evaluated with the Cohn–Kanade database, our system achieves the best accuracies when the ensemble classifier is applied, and outperforms other research reported in the literature with 96.8% for direct similarity based optimization and 97.4% for the Pareto-based feature selection. Cross-database evaluation with frontal images from the MMI database has also been conducted to further prove system efficiency where it achieves 97.5% for Pareto-based approach and 90.7% for direct similarity-based feature selection and outperforms related research for MMI. When evaluated with 90° side-view images extracted from the videos of the MMI database, the system achieves superior performances with >80% accuracies for both optimization algorithms. Experiments with other weighting and meta-learning combination methods for the construction of ensembles are also explored with our proposed ensemble showing great adpativity to new test data stream for cross-database evaluation. In future work, we aim to incorporate other filtering techniques and evolutionary algorithms into the optimization models to further enhance the recognition performance.

Improving Classification of Protein Interaction Articles Using Context Similarity-Based Feature Selection.

Protein interaction article classification is a text classification task in the biological domain to determine which articles describe protein-protein interactions. Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance. Many existing feature selection methods are based on the statistical measure of document frequency and term frequency. One potential drawback of these methods is that they treat features separately. Hence, first we design a similarity measure between the context information to take word cooccurrences and phrase chunks around the features into account. Then we introduce the similarity of context information to the importance measure of the features to substitute the document and term frequency. Hence we propose new context similarity-based feature selection methods. Their performance is evaluated on two protein interaction article collections and compared against the frequency-based methods. The experimental results reveal that the context similarity-based methods perform better in terms of the F1 measure and the dimension reduction rate. Benefiting from the context information surrounding the features, the proposed methods can select distinctive features effectively for protein interaction article classification.

Topological Similarity-Based Feature Selection for Graph Classification

Topological Similarity-Based Feature Selection for Graph Classification

Predictor output sensitivity and feature similarity-based feature selection

This paper is concerned with a feature selection technique capable of generating an efficient feature set in a few selection steps. The feature saliency measure proposed is based on two factors, namely, the fuzzy derivative of the predictor output with respect to the feature and the similarity between the feature being considered and the feature set. The use of the fuzzy derivative enables modelling the vagueness that occurs in estimating the predictor output sensitivity. The feature similarity measure employed allows avoiding utilization of very redundant features. The experimental investigations performed on five real world problems have shown the effectiveness of the feature selection technique proposed. The technique developed removed a large number of features from the original data sets without reducing the classification accuracy of a classifier. In contrast, the accuracy of the classifiers utilizing the reduced feature sets was higher than those exploiting all the original features.