RDPVR: Random Data Partitioning with Voting Rule for Machine Learning from Class-Imbalanced Datasets

Since most classifiers are biased toward the dominant class, class imbalance is a challenging problem in machine learning. The most popular approaches to solving this problem include oversampling minority examples and undersampling majority examples. Oversampling may increase the probability of overfitting, whereas undersampling eliminates examples that may be crucial to the learning process. We present a linear time resampling method based on random data partitioning and a majority voting rule to address both concerns, where an imbalanced dataset is partitioned into a number of small subdatasets, each of which must be class balanced. After that, a specific classifier is trained for each subdataset, and the final classification result is established by applying the majority voting rule to the results of all of the trained models. We compared the performance of the proposed method to some of the most well-known oversampling and undersampling methods, employing a range of classifiers, on 33 benchmark machine learning class-imbalanced datasets. The classification results produced by the classifiers employed on the generated data by the proposed method were comparable to most of the resampling methods tested, with the exception of SMOTEFUNA, which is an oversampling method that increases the probability of overfitting. The proposed method produced results that were comparable to the Easy Ensemble (EE) undersampling method. As a result, for solving the challenge of machine learning from class-imbalanced datasets, we advocate using either EE or our method.

Phylogenetic signal in characters from Aristotle’s History of Animals

The influential Greek philosopher Aristotle (384-322 BCE) is almost unanimously acclaimed as the founder of zoology. There is a consensus that he was interested in attributes of animals, but whether or not he tried to develop a zoological taxonomy remains controversial. Fürst von Lieven and Humar compiled a data matrix from Aristotle’s Historia animalium and showed, through a parsimony analysis published in 2008, that these data produced a hierarchy that matched several taxa recognized by Aristotle. However, their analysis leaves some questions unanswered because random data can sometimes yield fairly resolved trees. In this study, we update the scores of many cells and add four new characters to the data matrix (147 taxa scored for 161 characters) and quote passages from Aristotle’s Historia animalium to justify these changes. We confirm the presence of a phylogenetic signal in these data through a test using skewness in length distribution of a million random trees, which shows that many of the characters discussed by Aristotle were systematically relevant. Our parsimony analyses on the updated matrix recover far more trees than reported by Fürst von Lieven and Humar, but their consensus includes many taxa that Aristotle recognized and apparently named for the first time, such as selachē (selachians) and dithyra (Bivalvia Linnaeus, 1758). This study suggests that even though taxonomy was obviously not Aristotle’s chief interest in Historia animalium, it was probably among his secondary interests. These results may pave the way for further taxonomic studies in Aristotle’s zoological writings in general. Despite being almost peripheral to Aristotle’s writings, his taxonomic contributions are clearly major achievements.

Projections of Tropical Fermat-Weber Points

This paper is motivated by the difference between the classical principal component analysis (PCA) in a Euclidean space and the tropical PCA in a tropical projective torus as follows. In Euclidean space, the projection of the mean point of a given data set on the principle component is the mean point of the projection of the data set. However, in tropical projective torus, it is not guaranteed that the projection of a Fermat-Weber point of a given data set on a tropical polytope is a Fermat-Weber point of the projection of the data set. This is caused by the difference between the Euclidean metric and the tropical metric. In this paper, we focus on the projection on the tropical triangle (the three-point tropical convex hull), and we develop one algorithm and its improved version, such that for a given data set in the tropical projective torus, these algorithms output a tropical triangle, on which the projection of a Fermat-Weber point of the data set is a Fermat-Weber point of the projection of the data set. We implement these algorithms in R language and test how they work with random data sets. We also use R language for numerical computation. The experimental results show that these algorithms are stable and efficient, with a high success rate.

Application three-valued logic in symmetric block encryption algorithms

The issues of limiting the use of binary logic for the further development of science engineering are discussed. The effectiveness of the use of the ternary number system at this stage in the development of information technologies is substantiated and shown. A method is proposed for increasing the informational entropy of plaintext by adding random data using ternary logic in the process of symmetric encryption. To reliably hide the added random data, the first transforming function is proposed to choose gamming with a key.

Optimal Image Steganography Content Destruction Techniques

The paper presents two approaches for destroying steganogrphy content in an image. The first is the overwriting approach where a random data can be written again over steganographic images whereas the second approach is the denoising approach. With the second approach two kinds of destruction techniques have been adopted these are filtering and discrete wavelet techniques. These two approaches have been simulated and evaluated over two types of hiding techniques, Least Significant Bit LSB technique and Discrete Cosine Transform DCT technique. The results of the simulation show the capability of both approaches to destroy the hidden information without any alteration to the cover image except the denoising approach enhance the PSNR in any received image even without hidden information by an average of 4dB.

Analisis Faktor-Faktor yang Berpengaruh terhadap Kemitraan Sapi Perah dengan Metode Structure Equation Modelling

This research aims to determine the determinants that affect the partnership between dairy farmers and KSU Karya Nugraha. This research was carried out in Cigugur District, Kuningan Regency, West Java, which was determined purposively, the largest milk-producing cooperative in Kuningan Regency. Data collection was carried out from January to September 2020. The study used the dependent variable of partnership, and independent variables are communication, cooperation, trust, and commitment. The survey was conducted to collect information using questionnaires with the number of respondents 42 people selected at random. Data is processed by the SEM-PLS method. The results showed only one insignificant determinant of the partnership was cooperation. Communication, trust, and commitment have a significant effect on alliances. Research has also found that communication and responsibility have a significant influence on collaboration and trust

Socio-economic factors of farmers in implementing the profit-sharing system in beef cattle business

One of the regency in South Sulawesi Province that implements a profit-sharing system is Bone Regency which has been done by farmers for a long time and has been carried out from generation to generation. This system is one of the local wisdoms for the community in managing the beef cattle business. The aim of this study was to analyze the socio-economic factors of farmers in implementing a profit-sharing system in beef cattle business in Bone Regency. The type of the research was an explanatory study with a sample of 175 farmers who were taken simple random. Data were collected through interviews with the help of a questionnaire where each variable measured used a Likert scale, namely 1 = disagree, 2 = disagree less, 3 = agree and analyzed using multiple linear regression. The results of this study indicated that socio-economic factors of farmers have a significant effect on the profit-sharing system in beef cattle business in Bone Regency.