Attribute selection for heterogeneous data based on information entropy

The central nervous system needs to coordinate multiple muscles during postural control. Functional coordination is established through the neural circuitry that interconnects different muscles. Here we used multivariate information decomposition of multichannel EMG acquired from 14 healthy participants during postural tasks to investigate the neural interactions between muscles. A set of information measures were estimated from an instantaneous linear regression model and a time-lagged VAR model fitted to the EMG envelopes of 36 muscles. We used network analysis to quantify the structure of functional interactions between muscles and compared them across experimental conditions. Conditional mutual information and transfer entropy revealed sparse networks dominated by local connections between muscles. We observed significant changes in muscle networks across postural tasks localized to the muscles involved in performing those tasks. Information decomposition revealed distinct patterns in task-related changes: unimanual and bimanual pointing were associated with reduced transfer to the pectoralis major muscles, but an increase in total information compared to no pointing, while postural instability resulted in increased information, information transfer and information storage in the abductor longus muscles compared to normal stability. These findings show robust patterns of directed interactions between muscles that are task-dependent and can be assessed from surface EMG recorded during static postural tasks. We discuss directed muscle networks in terms of the neural circuitry involved in generating muscle activity and suggest that task-related effects may reflect gain modulations of spinal reflex pathways.

Metaphor is to express another thing through one thing, it is not only a rhetorical means, but also embodies a kind of analogical cognition and way of thinking of people. In recent years, metaphors have made more and more progress in Chinese language recognition, so their status has gradually risen. Chinese language sentiment analysis is a relatively difficult problem in language processing. Chinese language semantics is ambiguous and complex, so it is a great challenge to construct Chinese Emotion structure. In this paper, Chinese nouns and Chinese verbs are used as test samples to conduct metaphor test calculation. The test results of mutual information and information entropy show that for the sample metaphor calculation, the correct rate of individual words is less than 70%. The ratio between the macro-average of gerund words and the baseline fluctuates relatively large. The metaphor recognition of nouns is about 20% higher than the original test results and about 10% higher for verbs. It shows that under the action of mutual information and information entropy, the metaphor recognition performance of the basic structure of Chinese Emotion constructed from the perspective of metaphor is improved, and further illustrates the feasibility of mutual information and information entropy algorithm for building the basic structure.

Editorial| July 2023 Anesthesia and the Merry-go-round of Information in the Brain Jamie W. Sleigh, M.D.; Jamie W. Sleigh, M.D. 1Department of Anaesthesiology, Waikato Clinical Campus, Faculty of Medical and Health Sciences, University of Auckland, Hamilton, New Zealand. Search for other works by this author on: This Site PubMed Google Scholar Logan Voss, Ph.D. Logan Voss, Ph.D. 2Department of Anaesthesiology, Waikato Clinical Campus, Faculty of Medical and Health Sciences, University of Auckland, Hamilton, New Zealand. Search for other works by this author on: This Site PubMed Google Scholar Author and Article Information This editorial accompanies the article on p. 49. Accepted for publication March 30, 2023. Address correspondence to Dr. Sleigh: jamie.sleigh@waikatodhb.health.nz Anesthesiology July 2023, Vol. 139, 4–5. https://doi.org/10.1097/ALN.0000000000004571 Connected Content Article: Measures of Information Content during Anesthesia and Emergence in the Caenorhabditis elegans Nervous System Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Cite Icon Cite Get Permissions Search Site Citation Jamie W. Sleigh, Logan Voss; Anesthesia and the Merry-go-round of Information in the Brain. Anesthesiology 2023; 139:4–5 doi: https://doi.org/10.1097/ALN.0000000000004571 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll PublicationsAnesthesiology Search Advanced Search Topics: brain It is widely believed that anesthesia is a syndrome caused by drug-induced disruption of coordination within and between neuronal networks. The underlying teleological assumption is that normal function of the central nervous system requires precise electrical and chemical communication between neurons, and communication implies flow of “information.” In this issue of Anesthesiology is an article by Chang et al.1 in which isoflurane was given to a nematode (Caenorhabditis elegans), and the resultant real-time changes in activity of about half of its 302 neurons were visualized using high-resolution calcium fluorescence imaging. In this study, the authors focused on how anesthesia alters interneuronal coupling by measuring the entropy of the calcium transients in the imaged neurons. The meaning of the word “entropy” is intuitively difficult. It is often used as a synonym for disorder, uncertainty, or unpredictability. To take the extreme examples: if a neuron has no activity (a... You do not currently have access to this content.

For an information system the attribute reduction is one key to choose some important indexes, but the core attribute is the foundation for the present heuristic attribute reduction algorithms of rough set theory based on discernibility matrix and mutual information theory. When we apply these algorithms on the non-core information system, there will be the following problems, such as too much calculation problem, excessive reduction, or insufficient reduction. According to the characteristics of non-core information system, we propose one new heuristic attribute reduction algorithm based on mutual information, in which the evaluation of attribute important degree depends on the increment of mutual information and information entropy. And the attribute with the largest attribute important degree is selected for the initial core attribute, by which the problem that the random selection attribute cause the computational complexity is solved. By the proposed algorithm we cannot only improve the efficiency of attribute reduction, but decrease the number of attribute reduction. Both the theoretic analysis and the simulation experiments verify that the proposed algorithm is validity

Presently, many researches have been carried out on rough set based data reduction. However, this method encounters a problem when dealing with real-valued data and fuzzy information. By lucubrating the theory of fuzzy rough set and utilizing the definition of information entropy presented in literature [5], the information entropy model of fuzzy rough set has been constructed. Then the conditional information entropy of attributes is adopted to measure the significance of attributes. On this condition, a heuristic fuzzy-rough data reduction method based on information entropy (E-FRDR) has been put forward. Finally, the method is validated by an example that indicates the method is feasible.

Based on the complement behavior of information gain, a new definition of information entropy is proposed along with its justification in rough set theory. Some properties of this definition imply those of Shannon's entropy. Based on the new information entropy, conditional entropy and mutual information are then introduced and applied to knowledge bases. The new information entropy is proved to also be a fuzzy entropy.

Community structure is one of the important characteristics of complex networks. Community discovery has important theoretical significance and practical value. At present, accurate estimation of the number of communities is the key and difficult issue in community discovery. Inspired by the idea of Bayesian inference, this paper deduces the relationship between maximum possible partition and average mutual information and information entropy, and proposes a framework algorithm Number_Accurate_Estimation (short for NAE) and a concrete implementation to estimate the community number. Several typical algorithms for community number estimation are compared on several typical data sets, and experiments show the effectiveness of our method.

Some disadvantages should be discussed deeply for the current reduction algorithms. To eliminate these limitations of classical algorithms based on positive region and conditional information entropy, a new conditional entropy, which could reflect the change of decision ability objectively, was defined with separating consistent objects form inconsistent objects. To select optimal attribute reduction, the judgment theorem of reduction with an inequality was investigated. Condition attributes were considered to estimate the significance for decision classes, and a complete heuristic algorithm was designed and implemented. Finally, through analyzing the given example, the proposed heuristic information is better and more efficient than the others. Comparing the proposed algorithm with these current algorithms through discrete data sets from UCI Machine Learning Repository, the experimental results prove its validity, which enlarges the applied area of rough set. from inconsistent objects, and the corresponding judgment theorem with an inequality is proposed for searching the minimal or optimal reduction of knowledge. With this new conditional entropy, we design a new heuristic algorithm of attribute reduction, which can reduce the computational complexity. Its time complexity is O(|C| 2 |U|), where |C| is the number of condition attributes, and |U| is the number of objects. In the large decision tables, since there usually is |U| >> |C|, the new attribute reduction algorithm is better than the presented algorithms above. Finally, the implemented algorithms are experimented with an example and four data sets from UCI Machine Learning Repository are given to illustrate the effectiveness of the new proposed algorithm through intercomparsion. The experiment results demonstrate its usefulness and are analyzed for further research.

The disadvantages of the recent reduction algorithms are analyzed deeply. A new measure to knowledge and rough set is introduced to discuss the rough entropy of knowledge and the roughness of rough set. Based on this entropy, the new significance of attribute is defined and a heuristic algorithm of knowledge reduction is proposed and compared with two methods of attribute reduction which are based on the positive region and the conditional information entropy respectively. The result shows that the proposed heuristic information is better and more efficient than the others, and is greatly effective and feasible in searching the minimal or optimal reduction. Theoretical analysis and experimental results indicate that the time complexity of this reduction algorithm is less than that based on the current positive region and the conditional information entropy.

In this paper, we propose the selection of most suitable mother wavelet function for Turkish phonemes using discrete wavelet transform. The determination of most similar mother wavelet function to the signal has been a challenge in speech processing. The optimum mother wavelet function for Turkish phonemes have been determined by using quantitative measures which are energy and Shannon entropy, information theoretic measures which are joint entropy, conditional entropy, mutual information, and relative entropy from wavelet coefficients of the phonemes. In this study, 101 potential functions were investigated to determine the most appropriate mother wavelet. Experimental results show that the most appropriate wavelet functions for /ç/ and /ş/ phonemes which are unvoiced fricatives have been found as Bi-orthogonal 3.9 and Bi-orthogonal 5.5, respectively. By considering all the results, it is seen that the Bi-orthogonal 3.1 and Discrete Meyer wavelet functions are the most suitable mother wavelets for all other phonemes.

Feature selection in a neighborhood decision information system with application to single cell RNA data classification

The construction of the economic management information system ensures the flow and sharing of information, business coordination, and scientific decision-making between economic functions. Therefore, project construction is absolutely necessary and urgent. Project management has achieved significant results in terms of shortening project time and reducing management costs. Information system is an important model information structure in artificial intelligence. This paper focuses on the design of a fusion method for economic management problems based on decision information systems. This paper summarizes the value of the whole project through the research on the project management of the economic management information system. The results of this study show that when the threshold is fixed, the information granularity Gtheta and rough entropy (Er) theta decrease monotonically with increasing attribute subset cardinality. Meanwhile, θ-information granularity Etheta and θ-information entropy Htheta monotonically increase with the increase of attribute subset cardinality, which indicates that when attribute subset cardinality increases, the uncertainty of the information system decreases.

Over the last six decades, the representation of error exponent functions for data transmission through noisy channels at rates below capacity has seen three distinct approaches: (1) Through Gallager’s functions (with and without cost constraints); (2) large deviations form, in terms of conditional relative entropy and mutual information; (3) through the -mutual information and the Augustin–Csiszár mutual information of order derived from the Rényi divergence. While a fairly complete picture has emerged in the absence of cost constraints, there have remained gaps in the interrelationships between the three approaches in the general case of cost-constrained encoding. Furthermore, no systematic approach has been proposed to solve the attendant optimization problems by exploiting the specific structure of the information functions. This paper closes those gaps and proposes a simple method to maximize Augustin–Csiszár mutual information of order under cost constraints by means of the maximization of the -mutual information subject to an exponential average constraint.

Reduction is one of the key problem in rough set theory due to its applications in data mining, rule induction, classification, etc.. In this paper the reductions for a set-valued decision information system(DIS) are studied. The judgment theorem and the discernibility matrix of the generalized decision reduct in a set-valued DIS are given, and the relationships among the generalized decision reduct and alternative types of knowledge reduction in set-valued DIS are investigated. It is proved that the reduct in the consistent set-valued DIS is equivalent to the generalized decision reduct, and the possible reduct in the inconsistent set-valued DIS is equivalent to the generalized decision reduct. The judgment theorem and the discernibility matrix associated with the possible reduct are also established.

A multi-robot gas source localization (GSL) based on multi-robot using conditional information entropy is proposed. In this method, two key point of multi-robot GSL is discussed and solved. The one is how to combined gas measurement of single robot; the other is using the combined result driving the robot to move. Here, we proposed a method base on distributed particle filtering to solve key point one, and using the conditional information entropy gradient to determine the motion of the robot, and then the key point two is solved. At last, three robots to be used for the experiment in the outdoor natural environment, The results validate the proposed algorithm.