A novel weighted fuzzy c-means based on feature weight learning

In feature weighted fuzzy c-means algorithms, there exist two challenges when the feature weighting techniques are used to improve their performances. On one hand, if the values of feature weights are learnt in advance, and then fixed in the process of clustering, the learnt weights might be lack of flexibility and might not fully reflect their relevance. On the other hand, if the feature weights are adaptively adjusted during the clustering process, the algorithms maybe suffer from bad initialization and lead to incorrect feature weight assignment, thus the performance of the algorithms may degrade the in some conditions. In order to ease these problems, a novel weighted fuzzy c-means based on feature weight learning (FWL-FWCM) is proposed. It is a hybrid of fuzzy weighted c-means (FWCM) algorithm with Improved FWCM (IFWCM) algorithm. FWL-FWCM algorithm first learns feature weights as priori knowledge from the data in advance by minimizing the feature evaluation function using the gradient descent technique, then iteratively optimizes the clustering objective function which integrates the within weighted cluster dispersion with a term of the discrepancy between the weights and the priori knowledge. Experiments conducted on an artificial dataset and real datasets demonstrate the proposed approach outperforms the-state-of-the-art feature weight clustering methods. The convergence property of FWL-FWCM is also presented.

EXPRESS: Correlation and Difference Between Raman Spectral Characteristic and Feature Evaluation for Leukocytes and Tumor Cells

Tumor detection supported by Raman spectroscopy is becoming increasingly popular, yet the relevance of spectral variation and feature selection retains unclear. Here we determined the correlation and difference between spectral characteristic and feature evaluation for leukocytes and tumor cells. Some peaks were found to show noticeable spectral differences, and their intensity distributions were investigated, finding using Log-Normal distribution to describe Raman intensity pattern may be more appropriate. Further the importance of all Raman features was calculated, where some other peak features occupied the top status. By surveying the intensity variation and feature evaluation for those peaks, we concluded the peak with the highest importance does not correspond to the peak location with the most noticeable intensity difference in spectra. Moreover, the peak-intensity-ratio of I₁₅₁₇/I₇₁₉ associated with protein to nucleic acid level presented the maximum separation, thus it can be recognized as a special indicator to develop an alternative cancer detection. It is inspiring to introduce advanced statistical models into bio-spectroscopic fields but those intrinsic spectral variations rather than classification performance should be valued. Our explorations can provide possibilities to reveal the essences within tumor carcinogenesis based on Raman spectroscopy, further overwhelming the obstacles during the translation into clinical applications.

Location prediction for facility placement by incorporating multi-characteristic information

In the course of recommending locations for establishing new facilities on urban planning or commercial programming, the location prediction offers the optimal candidates, which maximizes the number of served customers or minimize customer inconvenience, therefore brings the maximum profits. In most existing studies, only the spatial-temporal features are recognized to evaluate the location popularity, where social relationships of customers, which are significant factors for popularity assessing, have been ignored. Additionally, current researches also fail to take capacities and categories of the facilities into consideration. To overcome the drawbacks, we introduce a novel model of Multi-characteristic Information based Top-k Location Prediction (MITLP), it captures the spatio-temporal behaviors of customers based on historical trajectories, exploits the social relevancy from their friend relationships, as well as examines the category competitiveness of specific facilities thoroughly. Subsequently, by drawing on the feature evaluation and popularity quantization, MITLP will be implemented within a hybrid B-tree-liked recommending framework, Constrained Location and Social-Trajectory Clustered forest (CLSTC-forest), which can not only produce better performance in practice but also address the facility service constraints. Finally, extensive experiments conducted on real-world datasets demonstrate the higher efficiency and effectiveness of the proposed model.

An Entropy-Weighting Method for Efficient Power-Line Feature Evaluation and Extraction from LiDAR Point Clouds

Power-line inspection is an important means to maintain the safety of power networks. Light detection and ranging (LiDAR) technology can provide high-precision 3D information about power corridors for automated power-line inspection, so there are more and more utility companies relying on LiDAR systems instead of traditional manual operation. However, it is still a challenge to automatically detect power lines with high precision. To achieve efficient and accurate power-line extraction, this paper proposes an algorithm using entropy-weighting feature evaluation (EWFE), which is different from the existing hierarchical-multiple-rule evaluation of many geometric features. Six significant features are selected (Height above Ground Surface (HGS), Vertical Range Ratio (VRR), Horizontal Angle (HA), Surface Variation (SV), Linearity (LI) and Curvature Change (CC)), and then the features are combined to construct a vector for quantitative evaluation. The feature weights are determined by an entropy-weighting method (EWM) to achieve optimal distribution. The point clouds are filtered out by the HGS feature, which possesses the highest entropy value, and a portion of non-power-line points can be removed without loss of power-line points. The power lines are extracted by evaluation of the other five features. To decrease the interference from pylon points, this paper analyzes performance in different pylon situations and performs an adaptive weight transformation. We evaluate the EWFE method using four datasets with different transmission voltage scales captured by a light unmanned aerial vehicle (UAV) LiDAR system and a mobile LiDAR system. Experimental results show that our method demonstrates efficient performance, while algorithm parameters remain consistent for the four datasets. The precision F value ranges from 98.4% to 99.7%, and the efficiency ranges from 0.9 million points/s to 5.2 million points/s.

Multi-feature evaluation of financial contagion

Financial contagion refers to the spread of market turmoils, for example from one country or index to another country or another index. It is standardly assessed by modelling the evolution of the correlation matrix, for example of returns, usually after removing univariate dynamics with the GARCH model. However, significant events like crises visible in one financial market are usually reflected in other financial markets/countries simultaneously in several dimensions, i.e., in general, entire distributions of returns in other markets are affected. These distributions are determined/described by their expected value, variance, skewness, kurtosis and other statistics that determine the shape of the distribution function of returns, which can be based on higher (mixed) moments. These descriptive statistics are not constant over time, and, moreover, they can interreact within the given market and among the markets over time. In this article we propose, and use for the daily values of five indexes (CAC40, DAX30, DJIA, FTSE250 and WIG20) over the time period 2006–2017, a new, simple and computationally inexpensive methodology to automatically extend contagion evaluation from the evolution of the correlation matrix to the evolution of multiple higher mixed moments as well. Specifically, the joint distribution of normalized variables for each pair of indexes is modeled as a polynomial with time evolving coefficients estimated using an exponential moving average. As we can obtain any arbitrary number of evolving mixed moments this way, its dimensionality reduction using PCA (principal component analysis) is also discussed, obtaining a lower number of dominating and relatively independent features, which can each be interpreted through a polynomial that describes the corresponding perturbation of joint distribution. We obtain features that describe the interrelations among stock markets in several dimensions and that provide information about the current stage of crisis and the strength of the contagion process.