Proposed Feature Ranking Research Articles

Multi-label feature ranking with ensemble methods

In this paper, we propose three ensemble-based feature ranking scores for multi-label classification (MLC), which is a generalisation of multi-class classification where the classes are not mutually exclusive. Each of the scores (Symbolic, Genie3 and Random forest) can be computed from three different ensembles of predictive clustering trees: Bagging, Random forest and Extra trees. We extensively evaluate the proposed scores on 24 benchmark MLC problems, using 15 standard MLC evaluation measures. We determine the ranking quality saturation points in terms of the ensemble sizes, for each ranking-ensemble pair, and show that quality rankings can be computed really efficiently (typically 10 or 50 trees suffice). We also show that the proposed feature rankings are relevant and determine the most appropriate ensemble method for every feature ranking score. We empirically prove that the proposed feature ranking scores outperform current state-of-the-art methods in the quality of the rankings (for the majority of the evaluation measures), and in time efficiency. Finally, we determine the best performing feature ranking scores. Taking into account the quality of the rankings first and—in the case of ties—time efficiency, we identify the Genie3 feature ranking score as the optimal one.

Feature selection for predicting tool wear of machine tools

In this study, the vibration transmitted solely from a spindle to the worktable is proposed to be a crucial feature of wear prediction models for machine tools. To validate the effectiveness of the proposed feature, a feature ranking and screening methodology was also used for developing a tool wear prediction model. First, the features extracted from vibration signals were ranked according to their contributions to tool wear prediction. The features were then filtered through a screening process based on singular value decomposition to eliminate redundant features, which exhibited collinearity with features of higher rankings. The aim of the aforementioned steps was to use a relatively small number of highly appropriate features to create an accurate real-time tool wear prediction model. The results indicated that the accuracy of the tool wear prediction model based on the proposed feature ranking and screening methodology is higher than that of models without feature ranking or screening. Moreover, the proposed feature was proven to be more important and effective than other features.

An Efficient Cardiac Arrhythmia Onset Detection Technique Using a Novel Feature Rank Score Algorithm.

The interpretation of various cardiovascular blood flow abnormalities can be identified using Electrocardiogram (ECG). The predominant anomaly due to the blood flow dynamics leads to the occurrence of cardiac arrhythmias in the cardiac system. In this work, estimation of cardiac output (CO) parameter using blood flow rate analysis is carried out, which is a vital parameter to identify the subjects with left- ventricular arrhythmias (LVA). In particular, LVA is a resultant component of characteristic changes in blood rheology (blood flow rate). The CO is an intrinsic parameter derived from the stroke volume (SV) characterized by end-diastolic/systolic volumes (EDV/ESV) and heart rate. The pumping of blood from left ventricle (LV) reconciles in to R-R intervals depicted on ECG, which are used for heart rate estimation. The deviation from the nominal values of CO implies that, the subject is more prone to LVA. Further, the identification of subjects with LVA is accomplished by computing the features from the ECG signals. The proposed Feature Ranking Score (FRS) algorithm employs different statistical parameters to label the score of the extracted features. The feature score enables the selection optimal features for classification. The optimal features are further given to the Least Square- Support Vector Machine (LS-SVM) classifier for training and testing phases. The signals are acquired from public domain MIT-BIH arrhythmia database, used for validating the proposed technique for identifying the LVA using blood flow.

An Optimization-Based Method for Feature Ranking in Nonlinear Regression Problems.

In this paper, we consider the feature ranking problem, where, given a set of training instances, the task is to associate a score with the features in order to assess their relevance. Feature ranking is a very important tool for decision support systems, and may be used as an auxiliary step of feature selection to reduce the high dimensionality of real-world data. We focus on regression problems by assuming that the process underlying the generated data can be approximated by a continuous function (for instance, a feedforward neural network). We formally state the notion of relevance of a feature by introducing a minimum zero-norm inversion problem of a neural network, which is a nonsmooth, constrained optimization problem. We employ a concave approximation of the zero-norm function, and we define a smooth, global optimization problem to be solved in order to assess the relevance of the features. We present the new feature ranking method based on the solution of instances of the global optimization problem depending on the available training data. Computational experiments on both artificial and real data sets are performed, and point out that the proposed feature ranking method is a valid alternative to existing methods in terms of effectiveness. The obtained results also show that the method is costly in terms of CPU time, and this may be a limitation in the solution of large-dimensional problems.

Using genetic programming for context-sensitive feature scoring in classification problems

Feature scoring is an avenue to feature selection that provides a measure of usefulness for the individual features of a classification task. Features are ranked based on their scores and selection is performed by choosing a small group of high-ranked features. Most existing feature scoring/ranking methods focus on the relevance of a single feature to the class labels regardless of the role of other features (context-insensitive). The paper proposes a genetic programming (GP)-based method to see how a set of features can contribute towards discriminating different classes. The features receive score in the context of other features participating in a GP program. The scoring mechanism is based on the frequency of appearance of each feature in a collection of GP programs and the fitness of those programs. Our results show that the proposed feature ranking method can detect important features of a problem. A variety of different classifiers restricted to just a few of these high-ranked features work well. The proposed scoring-ranking mechanism can also shrink the search space of size O(2 n ) of subsets of features to a search space of size O(n) in which there are points that are very likely to improve the classification performance.

Protein Superfamily Classification Using Fuzzy Rule-Based Classifier

In this paper, we have proposed a fuzzy rule-based classifier for assigning amino acid sequences into different superfamilies of proteins. While the most popular methods for protein classification rely on sequence alignment, our approach is alignment-free and so more human readable. It accounts for the distribution of contiguous patterns of n amino acids ( n-grams) in the sequences as features, alike other alignment-independent methods. Our approach, first extracts a plenty of features from a set of training sequences, then selects only some best of them, using a proposed feature ranking method. Thereafter, using these features, a novel steady-state genetic algorithm for extracting fuzzy classification rules from data is used to generate a compact set of interpretable fuzzy rules. The generated rules are simple and human understandable. So, the biologists can utilize them, for classification purposes, or incorporate their expertise to interpret or even modify them. To evaluate the performance of our fuzzy rule-based classifier, we have compared it with the conventional nonfuzzy C4.5 algorithm, beside some other fuzzy classifiers. This comparative study is conducted through classifying the protein sequences of five superfamily classes, downloaded from a public domain database. The obtained results show that the generated fuzzy rules are more interpretable, with acceptable improvement in the classification accuracy.