Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

Classification is a crucial process in data mining, data science, machine learning, and the applications of natural language processing. Classification methods distinguish the correlation between the data and the output classes. In single-label classification (SLC), each input sample is associated with only one class label. In certain real-world applications, data instances may be assigned to more than one class. The type of classification which is required in such applications is known as multi-label classification (MLC). In MLC, each sample of data is associated with a set of labels. Due to the presence of multiple class labels, the SLC learning process is not applicable to MLC tasks. Many solutions to the multi-label classification problem have been proposed, including BR, FS-DR, and LLSF. But, these methods are not as accurate as they could be. In this paper, a new multi-label classification method is proposed based on graph representation. A feature selection technique and the Q-learning method are employed to increase the accuracy of the proposed algorithm. The proposed multi-label classification algorithm is applied to various standard multi-label datasets. The results are compared with state-of-the-art algorithms based on the well-known performance evaluation metrics. Experimental results demonstrated the effectiveness of the proposed model and its superiority over the other methods.

Let G be a graph with vertex set V (G) and edge set X (G) and consider the set A = {0, 1}. A mapping l: V (G)®A is called binary vertex labeling of G and l (v) is called the label of the vertex v under l. In this paper we introduce a new kind of graph energy for the binary labeled graph, the labeled graph energy El (G). It depends on the underlying graph G and on its binary labeling, upper and lower bounds for El (G) are established. The labeled energies of a number of well known and much studied families of graphs are computed.

Biological and biomedical microfluidic devices have been widely used to isolate rare cells. Although various techniques are available for analyzing rare cells, many are limited by high sample loss and low selectivity. Surface Acoustic Wave (SAW) technology offers a promising active approach for cellular analysis due to its simplicity, low cost, and compatibility with other microfluidic devices, making SAW-based microchips essential tools for manipulating rare cells. This review outlines the principles of acoustic isolation, introduces theoretical concepts and key equations, and examines recent experimental studies in this field. Findings indicate that SAW-based microfluidic devices have been effectively employed for manipulating, isolating, focusing, and patterning rare cells, achieving a cell separation efficiency of approximately 80-90%, though slightly lower than that for microparticles. This paper also discusses existing challenges and research gaps, recommending future directions such as the development of novel three-dimensional designs, optimization of acoustic devices through artificial intelligence, and the integration of acoustic methods with other techniques. 

Let G=(V, E) be a graph with p vertices and q edges. An acyclic graphoidal cover of G is a collection y of paths in G which are internally-disjoint and cover each edge of the graph exactly once. Let f:V® {1, 2, …, P} be a bijective labeling of the vertices of G. Let ­Gf be the directed graph obtained by orienting the edgesuv of G from u to v provided f(u)<f(v). If the set yf of all maximal directed paths in ­Gf, with directions ignored, is an acyclic graphoidal cover of G, then f is called a graphoidal labeling of G and G is called a label graphoidal graph and  hi=min {½yf½: f is a graphoidal labeling of G} is called the label graphoidal covering number of G. In this paper we characterize graphs for which (i) hi=q-m, where m is the number of vertices of degree 2 and (ii) hi=q. Also, we determine the value of label graphoidal covering number for unicyclic graphs.

In machine learning, models are derived from labeled training data where labels signify classes and features define sample attributes. However, noise from data collection can impair the algorithm’s performance. Blanco, Japón, and Puerto proposed mixed-integer programming (MIP) models within support vector machines (SVM) to handle label noise in training datasets. Nonetheless, it is imperative to underscore that their models demonstrate an observable escalation in the number of variables as sample size increases. The nonparallel support vector machine (NPSVM) is a bi-nary classification method that merges the strengths of both SVM and twin SVM. It accomplishes this by determining two nonparallel hyperplanes by solving two optimization problems. Each hyperplane is strategically po-sitioned to be closer to one of the classes while maximizing its distance from the other class. In this paper, to take advantage of NPSVM’s fea-tures, NPSVM-based relabeling (RENPSVM) MIP models are developed to deal with the label noises in the dataset. The proposed model adjusts observation labels and seeks optimal solutions while minimizing compu-tational costs by selectively focusing on class-relevant observations within an ϵ-intensive tube. Instances exhibiting similarities to the other class are excluded from this ϵ-intensive tube. Experiments on 10 UCI datasets show that the proposed NPSVM-based MIP models outperform their counter-parts in accuracy and learning time on the majority of datasets.