Minimum Distribution Support Vector Clustering

Support vector clustering (SVC) is a boundary-based algorithm, which has several advantages over other clustering methods, including identifying clusters of arbitrary shapes and numbers. Leveraged by the high generalization ability of the large margin distribution machine (LDM) and the optimal margin distribution clustering (ODMC), we propose a new clustering method: minimum distribution for support vector clustering (MDSVC), for improving the robustness of boundary point recognition, which characterizes the optimal hypersphere by the first-order and second-order statistics and tries to minimize the mean and variance simultaneously. In addition, we further prove, theoretically, that our algorithm can obtain better generalization performance. Some instructive insights for adjusting the number of support vector points are gained. For the optimization problem of MDSVC, we propose a double coordinate descent algorithm for small and medium samples. The experimental results on both artificial and real datasets indicate that our MDSVC has a significant improvement in generalization performance compared to SVC.

Correlation Analysis of Stocks and PMI Index Based on Logistic Regression Model

In order to explore the correlation between stocks and the PMI index, based on the generalized logistic loss and margin distribution, this paper designs a margin distribution logistic regression model that is easy to optimize, has robustness, and generalization ability, and gives a multiclass margin distribution logistic regression framework. This framework can be used to perform two-classification, multiclassification, and feature selection tasks. Moreover, this paper gives a training algorithm for margin distribution logistic regression on large-scale data sets through the pairwise stochastic gradient descent method. In addition, this paper combines the logistic regression model to construct a correlation analysis model between stocks and PMI index and uses the PMI data of the National Bureau of Statistics as a sample to design experiments to verify the performance of the system model constructed in this paper. From the experimental analysis, it can be seen that the algorithm constructed in this paper has a certain effect, and the strong correlation between PMI and stocks has been further verified.

Wavelet Kernel Least Squares Twin Support Vector Regression for Wind Speed Prediction

Wind energy is a potent yet freely available renewable energy. It is essential to estimate the wind speed (WS)precisely to makeaprecise estimation of wind power at wind power generating stations.Generally, the WS data is non-stationary. Wavelets have the potential to deal with the non-stationarilyindatasets. On the other hand, the prediction ability of primal least square support vector regression (PLSTSVR) has never been tested to best of our knowledge for WS prediction. Hence, in this work, wavelet kernel-based LSTSVR models are proposed for WS prediction. They are Morlet wavelet kernel LSTSVR and Mexican Hat wavelet kernel LSTSVR.HourlyWS data are collected from four different stations namely Chennai, Madurai, Salem and Tirunelveli in Tamil Nadu, India. The performance of the proposed models isevaluated using root mean square, mean absolute, symmetric mean absolute percentage, mean absolute scaled error and R2. The results of the proposed models are compared with twin support vector regression (TSVR), PLSTSVR and large-margin distribution machine-based regression (LDMR). Based on the results of the performance indicators, the performance of the proposed models is better when compared to other models.

Partial Multi-Label Optimal Margin Distribution Machine

Partial multi-label learning deals with the circumstance in which the ground-truth labels are not directly available but hidden in a candidate label set. Due to the presence of other irrelevant labels, vanilla multi-label learning methods are prone to be misled and fail to generalize well on unseen data, thus how to enable them to get rid of the noisy labels turns to be the core problem of partial multi-label learning. In this paper, we propose the Partial Multi-Label Optimal margin Distribution Machine (PML-ODM), which distinguishs the noisy labels through explicitly optimizing the distribution of ranking margin, and exhibits better generalization performance than minimum margin based counterparts. In addition, we propose a novel feature prototype representation to further enhance the disambiguation ability, and the non-linear kernels can also be applied to promote the generalization performance for linearly inseparable data. Extensive experiments on real-world data sets validates the superiority of our proposed method.

A Yes-Associated Protein (YAP) and Insulin-Like Growth Factor 1 Receptor (IGF-1R) Signaling Loop Is Involved in Sorafenib Resistance in Hepatocellular Carcinoma

The role of a YAP-IGF-1R signaling loop in HCC resistance to sorafenib remains unknown. Method: Sorafenib-resistant cells were generated by treating naïve cells (HepG2215 and Hep3B) with sorafenib. Different cancer cell lines from databases were analyzed through the ONCOMINE web server. BIOSTORM–LIHC patient tissues (46 nonresponders and 21 responders to sorafenib) were used to compare YAP mRNA levels. The HepG2215_R-derived xenograft in SCID mice was used as an in vivo model. HCC tissues from a patient with sorafenib failure were used to examine differences in YAP and IGF-R signaling. Results: Positive associations exist among the levels of YAP, IGF-1R, and EMT markers in HCC tissues and the levels of these proteins increased with sorafenib failure, with a trend of tumor-margin distribution in vivo. Blocking YAP downregulated IGF-1R signaling-related proteins, while IGF-1/2 treatment enhanced the nuclear translocation of YAP in HCC cells through PI3K-mTOR regulation. The combination of YAP-specific inhibitor verteporfin (VP) and sorafenib effectively decreased cell viability in a synergistic manner, evidenced by the combination index (CI). Conclusion: A YAP-IGF-1R signaling loop may play a role in HCC sorafenib resistance and could provide novel potential targets for combination therapy with sorafenib to overcome drug resistance in HCC.

MULFE: Multi-Label Learning via Label-Specific Feature Space Ensemble

In multi-label learning, label correlations commonly exist in the data. Such correlation not only provides useful information, but also imposes significant challenges for multi-label learning. Recently, label-specific feature embedding has been proposed to explore label-specific features from the training data, and uses feature highly customized to the multi-label set for learning. While such feature embedding methods have demonstrated good performance, the creation of the feature embedding space is only based on a single label, without considering label correlations in the data. In this article, we propose to combine multiple label-specific feature spaces, using label correlation, for multi-label learning. The proposed algorithm, mu lti- l abel-specific f eature space e nsemble (MULFE), takes consideration label-specific features, label correlation, and weighted ensemble principle to form a learning framework. By conducting clustering analysis on each label’s negative and positive instances, MULFE first creates features customized to each label. After that, MULFE utilizes the label correlation to optimize the margin distribution of the base classifiers which are induced by the related label-specific feature spaces. By combining multiple label-specific features, label correlation based weighting, and ensemble learning, MULFE achieves maximum margin multi-label classification goal through the underlying optimization framework. Empirical studies on 10 public data sets manifest the effectiveness of MULFE.

Marketing of Vegetable Seeds: Practice and Behavioral Inclinations of Vegetable Seed Sellers and Farmers in Selected Areas of Bangladesh

This research was based on a survey conducted in Bangladesh in three major seed-producing divisions, viz., Dhaka, Mymensingh, and Chittagong. Descriptive data was gathered by randomly selecting 100 peasants and 100 rural retailers for in-depth interviews. The general accounting approach was also used to assess profit and loss. The objective of the study was to analyze the marketing tendencies of vegetable seed farmers and sellers. The results showed a lack of market information, poor institutions and arrangements, poor marketing infrastructures, transportation system, and high and unfair profit margin distribution among the value chain actors with little share to the farmers in the vegetable seed market. These findings are indicators of poor marketing efficiency and thereby suboptimal operation of the seed marketing system. The significant determinants of market supply of vegetable seeds were found to be the average current price, age, the total size of land, farmers’ experience, sex, number of oxen, and access to market information. The determinants of demand for vegetable seeds—family size, purchase frequency, the average current price, income level, average expenditure on food and purchasing, profit or loss of vegetable seed farming—were found to be significant in the study. According to the findings of this report, vegetable seed sector in Bangladesh needs more government support, especially in terms of marketing policies in order to improve the current state of vegetable seed farming. Vegetable seed farming was not profitable due to a lack of technology and knowledge, as well as a lack of funding. With the existing status of infrastructure, the presence of middlemen is unavoidable. As a result, farmers have no alternative but to follow the orders of the middlemen, resulting in seed quality problems. Hence, the results are indicative of the measures that should be taken for production, market infrastructure, arrangements, and institutions to improve the functioning of the seed marketing system. It also proposes a vegetable seed distribution channel through which a cooperative community would serve as a collecting hub for a more efficient marketing scheme.

Analisis Pemasaran Biji Kopi Robusta di Desa Jambuwer Kecamatan Kromengan Kabupaten Malang

Indonesia is recorded as the third largest coffee producing country in the world. Robusta coffee is widely cultivated in Jambuwer Village Malang Regency. The purpose of this study was to determine: Robusta coffee marketing channels in Malang Regency. Calculating the amount of marketing margin, margin distribution and share of robusta coffee in Malang Regency. The results of this study indicate that there are four patterns of robusta coffee marketing channels, namely, marketing channel I: Farmers - Wholesalers - Retailers - Consumers. Marketing channel II: Farmers - Middlemen - Resellers - Consumers. Marketing channel III: Farmers - Middlemen - Consumers and marketing channels IV: Farmers - Middlemen - Companies. Meanwhile, the marketing margin for channel I is Rp. 4,000, marketing margin for channel II is Rp. 95,000, channel marketing margin is Rp. 95,000 and channel marketing margin is Rp. 2,000. The farmer's share value in marketing channel I was 84%, marketing channel II was 24%, marketing channel III was 24% and marketing channel IV was 91.7%. The result of the most efficient marketing channel for farmers is the marketing channel IV because it has a low marketing margin and a high farmer share value.