Showing papers on "Classifier chains published in 2021"

A new framework of multi-objective evolutionary algorithms for feature selection and multi-label classification of video data

Abstract: There are few studies in the literature to address the multi-objective multi-label feature selection for the classification of video data using evolutionary algorithms Selecting the most appropriate subset of features is a significant problem while maintaining/improving the accuracy of the prediction results This study proposes a framework of parallel multi-objective Non-dominated Sorting Genetic Algorithms (NSGA-II) for exploring a Pareto set of non-dominated solutions The subsets of non-dominated features are extracted and validated by multi-label classification techniques, Binary Relevance (BR), Classifier Chains (CC), Pruned Sets (PS), and Random k-Labelset (RAkEL) Base classifiers such as Support Vector Machines (SVM), J48-Decision Tree (J48), and Logistic Regression (LR) are performed in the classification phase of the algorithms Comprehensive experiments are carried out with local feature descriptors extracted from two multi-label data sets, the well-known MIR-Flickr dataset and a Wireless Multimedia Sensor (WMS) dataset that we have generated from our video recordings The prediction accuracy levels are improved by 636% and 257% for the MIR-Flickr and WMS datasets respectively while the number of features is significantly reduced The results verify that the algorithms presented in this new framework outperform the state-of-the-art algorithms

Classifier chains: A review and perspectives

Abstract: The family of methods collectively known as classifier chains has become a popular approach to multi-label learning problems. This approach involves linking together off-the-shelf binary classifiers in a chain structure, such that class label predictions become features for other classifiers. Such methods have proved flexible and effective and have obtained state-of-the-art empirical performance across many datasets and multi-label evaluation metrics. This performance led to further studies of how exactly it works, and how it could be improved, and in the recent decade numerous studies have explored classifier chains mechanisms on a theoretical level, and many improvements have been made to the training and inference procedures, such that this method remains among the state-of-the-art options for multi-label learning. Given this past and ongoing interest, which covers a broad range of applications and research themes, the goal of this work is to provide a review of classifier chains, a survey of the techniques and extensions provided in the literature, as well as perspectives for this approach in the domain of multi-label classification in the future. We conclude positively, with a number of recommendations for researchers and practitioners, as well as outlining a number of areas for future research.

Classifier chains for positive unlabelled multi-label learning

Abstract: In traditional multi-label setting it is assumed that all relevant labels are assigned to the given instance. In positive unlabelled setting, only some of relevant labels are assigned. The appearance of a label means that the instance is really associated with this label, while the absence of the label does not imply that this label is not proper for the instance. For example, when predicting multiple diseases in one patient, some diseases can be undiagnosed however it does not mean that the patient does not have these diseases. Classifier chains are one of the most popular and successful methods used in standard multi-label classification, mainly due to their simplicity and high predictive power. However, it turns out that adaptation of classifier chains to positive unlabelled framework is not straightforward, due to the fact that the true target variables are observed only partially and therefore they cannot be used directly to train the models in the chain. The partial observability concerns not only the current target variable in the chain but also the feature space, which additionally increases the difficulty of the problem. In this paper we investigate the possibility of using classifier chains in positive unlabelled setting. We propose two methods in which we modify classifiers in the chain in order to take into account partial data observability. In the first method (called CCPU) we scale the output probabilities of the consecutive classifiers in the chain. In the second method (called CCPUW) we minimize weighted empirical risk, with weights depending on prior probabilities of the target variables. Moreover, both methods use modified feature spaces. The predictive performance of the proposed methods is studied on real multi-label datasets for different positive unlabelled settings.

Variation-aware Analog Circuit Sizing with Classifier Chains

Abstract: In this work, a simulation-based optimization framework is proposed that determines the sizing of components of an analog circuit to meet target design specifications while also satisfying the robustness specifications set by the designer. The robustness is guaranteed by setting a limit on the standard deviations of the variations in the performance parameters of a circuit across all process and temperature corners of interest. Classifier chains are utilized that, in addition to modeling the relationship between inputs and outputs, learn the relationships among output labels. Additional design knowledge is inferred from the optimal ordering of the classifier chain. A case study is provided, where an LNA is designed in a 65 nm fabrication process. The corners of interest include the combination of the three temperatures of 20°C, 80°C, and 120°C, and the five process corners of typical-typical, slow-slow, fast-fast, slow-fast, and fast-slow. The adoption of classifier chains and the ensemble of classifier chains provides an improvement in the prediction accuracy as compared to the utilization of binary relevance. A qualified design solution is generated that satisfies both the performance and robustness specifications within 5 executed iterations of the design loop.

Acoustic Event Detection with Classifier Chains

Abstract: This paper proposes acoustic event detection (AED) with classifier chains, a new classifier based on the probabilistic chain rule. The proposed AED with classifier chains consists of a gated recurrent unit and performs iterative binary detection of each event one by one. In each iteration, the event’s activity is estimated and used to condition the next output based on the probabilistic chain rule to form classifier chains. Therefore, the proposed method can handle the interdependence among events upon classification, while the conventional AED methods with multiple binary classifiers with a linear layer and sigmoid function have placed an assumption of conditional independence. In the experiments with a real-recording dataset, the proposed method demonstrates its superior AED performance to a relative 14.80% improvement compared to a convolutional recurrent neural network baseline system with the multiple binary classifiers.

L2S-MirLoc: A Lightweight Two Stage MiRNA Sub-Cellular Localization Prediction Framework

Abstract: A comprehensive understanding of miRNA sub-cellular localization may leads towards better understanding of physiological processes and support the fixation of diverse irregularities present in a variety of organisms. To date, diverse computational methodologies have been proposed to automatically infer sub-cellular localization of miR-NAs solely using sequence information, however, existing approaches lack in performance. Considering the success of data transformation approaches in Natural Language Processing which primarily transform multi-label classification problem into multi-class classification problem, here, we introduce three different data transformation approaches namely binary relevance, label power set, and classifier chains. Using data transformation approaches, at 1st stage, multi-label miRNA sub-cellular localization problem is transformed into multi-class problem. Then, at 2nd stage, 3 different machine learning classifiers are used to estimate which classifier performs better with what data transformation approach for hand on task. Empirical evaluation on independent test set indicates that L2S-MirLoc selected combination based on binary relevance and deep random forest outperforms state-of-the-art performance values by significant margin.

Multi-label Diagnosis Algorithm for Arrhythmia Diseases Based on Improved Classifier Chains

Abstract: Electrocardiogram (ECG) has been proved to be the most common and effective approach to investigate arrhythmia. In clinical, a segment of ECG signal often indicates several arrhythmia diseases. Therefore, the automatic diagnosis algorithm of arrhythmia can be seen as a multi-label classification problem. In order to improve the classification accuracy, a method based on improved classifier chains is proposed in this paper. First, a deep neural network is pre-trained to extract the features of the ECG, and then multiple extreme random forest classifiers are used to construct a classifier chain in line with the process of clinical diagnosis, thereby completing the multi-label diagnosis of arrhythmia diseases. The experiment results show that compared with the method based on neural network, the subset accuracy of proposed method is improved from 76.62% to 83.94%, while other indicators are also improved.

Multi-label Chaining with Imprecise Probabilities

Abstract: We present two different strategies to extend the classical multi-label chaining approach to handle imprecise probability estimates. These estimates use convex sets of distributions (or credal sets) in order to describe our uncertainty rather than a precise one. The main reasons one could have for using such estimations are (1) to make cautious predictions (or no decision at all) when a high uncertainty is detected in the chaining and (2) to make better precise predictions by avoiding biases caused in early decisions in the chaining. We adapt both strategies to the case of the naive credal classifier, showing that this adaptations are computationally efficient. Our experimental results on missing labels, which investigate how reliable these predictions are in both approaches, indicate that our approaches produce relevant cautiousness on those hard-to-predict instances where the precise models fail.

Anemia Multi-label Classification Based on Problem Transformation Methods

Abstract: The CBC report is considered to be the most important report for assessing the overall health of the human body which is an important test for the diagnosis of diseases such as anemia, cancer, infections, vitamin, and mineral deficiencies. Anemia is a common health problem among people worldwide. Anemia is not a disease but a sign of a serious illness. Therefore, it can be prevented from serious diseases diagnosed at an earlier stage. The pattern of CBC parameters is found to be very complex. Therefore, a multi-label classification of the types of IDA, Vitamin B12, aplastic, and sickle cell anemia has been made by the machine learning problem transformation method which gives an idea of the type of anemia that is likely to occur due to the abnormal condition of CBC parameters. The use of the model can help predict anemia at an earlier stage. And serious diseases can be avoided. Ultimately, people can be saved from financial costs, kept mentally healthy, and concentration and regularity in teaching children can also be improved. This multi-label classification inspires the application of methods to classify these anemia types. Binary relevance, classifier chains, and label power set methods are used with different base classifications, and the results are analyzed. The results obtained by the SVM model based on the classifier chains method of multi-label classification have proved to be superior to other methods.