Multi-label classification has gained significant attention during recent years, due to the increasing number of modern applications associated with multi-label data. Despite its short life, different approaches have been presented to solve the task of multi-label classification. LIFT is a multi-label Classifier which utilizes a new strategy to multi-label learning by leveraging label-specific features. Label-specific features means that each class label is supposed to have its own characteristics and is determined by some specific features that are the most discriminative features for that label. LIFT employs clustering methods to discover the properties of data. More precisely, LIFT divides the training instances into positive and negative clusters for each label which respectively consist of the training examples with and without that label. It then selects representative centroids in the positive and negative instances of each label by k-means clustering and replaces the original features of a sample by the distances to these representatives. Constructing new features, the dimensionality of the new space reduces significantly. However, to construct these new features, the original features are needed. Therefore, the complexity of the process of multi-label classification does not diminish, in practice. In this paper, we make a modification on LIFT to reduce the computational burden of the Classifier and improve or at least preserve the performance of it, as well. The experimental results show that the proposed algorithm has obtained these goals, simultaneously.

Designing an effective criterion for selecting the best rule is a major problem in the process of implementing Fuzzy Learning Classifier (FLC) systems. Conventionally confidence and support or combined measures of these are used as criteria for fuzzy rule evaluation. In this paper new entities namely precision and recall from the field of Information Retrieval (IR) systems is adapted as alternative criteria for fuzzy rule evaluation. Several Different combinations of precision and recall are redesigned to produce a metric measure. These newly introduced criteria are utilized as a rule Selection mechanism in the method of Iterative Rule Learning (IRL) of FLC. In several experiments, three standard datasets are used (0 compare and contrast the novel IR based criteria with other previously developed measures. Experimental results illustrate the effectiveness of the proposed techniques in terms of classification performance and computational efficiency.

Individual classification models have recently been challenged by ensemble of Classifiers, also known as multiple Classifier system, which often shows better classification accuracy. In terms of merging the outputs of an ensemble of Classifiers, Classifier Selection has not attracted as much attention as Classifier fusion in the past, mainly because of its higher computational burden. In this paper, we propose a novel technique for improving Classifier Selection. In our method, the simple divide-and-conquer strategy is adapted in that a complex classification problem is divided into simpler binary sub-classification problems. We conduct extensive experiments on a series of multi-class datasets from the UCI (University of California, Irvine) repository and on odor database. The experimental results demonstrate the advanced performance of the proposed method.

Background: Liver cancer is one of the most common types of cancer, in which early detection plays a significant role in preventing progression and reducing mortality. Ultrasound is one of the methods of liver examination recommended by guidelines due to its performance in detecting focal liver lesions. These small lesions may be missed in the early stages or diagnosed only when the prognosis is poor. Objectives: This study aimed to implement the best classification model for two liver stages by extracting optimal feature subsets to be used in computer-aided diagnosis systems (CAD). Methods: The model classifies the liver into two stages using B-mode ultrasound images of the liver. It involves extracting statistical texture features utilizing discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM). This study applied two feature Selection methods: t-test and sequential forward floating Selection (SFFS). The subset of selected features was presented to the k-nearest neighbor Classifier for incorporation into a CAD system. Results: The accuracy, sensitivity, and specificity of the k-NN Classifier were 98.75%, 98.82%, and 99.1%, respectively. Conclusions: Image analysis approaches were successfully performed to extract and select useful features. Therefore, this model is recommended for classifying two liver stages, normal and HCC.

In modern prostheses, accurate processing of surface electromyogram (sEMG) signals has a significant effect on optimal muscle control. Although these signals are useful for diagnosing neuromuscular diseases, controlling prosthetic devices and detecting hand movements, non-robustness of EMG signal-based recognition will give rise to various movement disorders. In this paper, we present an optimal approach to classify EMG signals for hand gesture and movement recognition, whose purpose is to be used as an efficient method of diagnosing neuromuscular diseases, determining the type of treatment and physiotherapy. The main assumption of this study is to improve the accuracy of recognition and therefore, we proposed a novel hand gesture and movement recognition model consists of three steps: (1) EMG signal features extraction based on time-frequency domain and fractal dimension features; (2) feature Selection by soft ensembling of three procedures in which includes two sample T-tests, entropy and common wrapper feature reduction, and (3) classification based on kernel parameters optimization of SVM Classifier by using Gases Brownian Motion Optimization (GBMO) algorithm. Two UC2018 DualMyo and UCI datasets have been considered to evaluate the proposed model. The first dataset is used to classify eight hand gestures and the second dataset is employed for the classification of six types of movement. The experiment results and statistical tests reveal that the designed approach has desirable performance with an average accuracy of above 98% in both datasets. Contrary to similar methods that perform classifications in finite classes with high error rates, the integrated method has satisfactory accuracy, robustness and reliability. Not only the proposed method contributes to the design of prostheses, but also provides effective outcomes for rehabilitation applications and clinical diagnosis processes.

This study aims to employ supervised Advanced machine learning for the classification of lithological facies from geophysical log data in wells without drilling core samples. For this purpose, a dataset from seven wells in a training set from one of the oil fields in southern Iran has been utilized. This dataset includes natural gamma ray (SGR), corrected gamma ray (CGR), bulk density (RHOB), neutron porosity (NPHI), compressional wave slowness (DTSM), and shear wave slowness (DTCO), which directly influence the classification of geomechanical facies. These parameters are employed as independent variables, while lithological facies serve as the dependent variable for classification. This dataset pertains to depths ranging from 3000 to 4000 meters in the Ilam and Sarvak fractured limestone formations (Bangestan Limestone) of the subsurface. As the title suggests in this article, Initially, through artificial intelligence clustering methods and laboratory studies, these formations were categorized into five distinct lithological facies After this stage, eight supervised machine learning methods were employed, including Regression Logistic, K Neighbors Classifier, Decision Tree Classifier, Random Forest Classifier, Gaussian NB, Gradient Boosting Classifier, Extra Trees Classifier, and Support Vector Machine (SVM), to predict lithological facies in wells without existing classifications. The dataset of these wells underwent training and testing stages with each of these algorithms to construct an appropriate model. As a result, facies labels were predicted. The performance of the models was evaluated using multiple metrics including Accuracy, Precision, F1-Score, and Recall through confusion matrices and ROC curves. The Extra Trees Classifier, Gradient Boosting Classifier, and K Neighbors Classifier showed superior results among these methods. Finally, the model's performance in predicting lithological facies of unseen or out-of-sample wells was presented.

An Intelligent Particle Swarm Classifier (IPSClassifier) is proposed in this paper. This Classifier is described for finding the decision hyperplanes to classify patterns of different classes in the feature space using particle swarm optimization (PSO) algorithm. An intelligent fuzzy controller is designed to improve the performance and efficiency of proposed swarm intelligence based Classifier by adapting three important parameters of PSO (i.e., swarm size, neighborhood size, and constriction coefficient). Three pattern recognition problems with different feature vector dimensions were used to demonstrate the effectiveness of the proposed Classifier. They are the Iris data classification, the Wine data classification, and radar targets classification from backscattered signals. The experimental results show that the performance of the IPS-Classifier is comparable to or better than the k-nearest neighbor (k-NN) and multi-layer perceptron (MLP) Classifiers, which are two conventional Classifiers.