带权决策表的属性约简

属性约简是粗糙集理论的重要应用。考虑将决策表中的每行都作为一条决策规则时，若把表中出现相同决策规则的次数作为权，可得到带权决策表。提出了关于带权决策表的正域约简相应的辨识矩阵并给出了证明，从而得到了约简算法。相比于决策表中的正域约简时发现，通过将决策表转化为带权决策表后，再利用算法1进行约简时，其在一定程度上优于前者。提出了近似分类精度约简相应的辨识矩阵并给出了证明。对于2个算法，在选取的UCI数据集上进行了实验验证。通过实验进一步说明了所提出算法的可行性和有效性。     

Predicting how consumers perceive the naturalness of snacks: The usefulness of a simple index

For many consumers, it is important that food products be natural. Naturalness is a perceived property of food, but in the present study, we demonstrate that an objectively defined Food Naturalness Index (FNI) predicts perceived naturalness with a high degree of accuracy. A sample of 179 participants ranked 28 snacks, ranging from least natural to the most natural. Correlations on aggregated and individual levels suggest that perceived naturalness is strongly associated with the FNI. The food industry could therefore use the FNI to predict perceived naturalness during the product development phase of snacks, and it might also be a promising tool for regulating the use of naturalness claims in food marketing.     

果品分级检测技术的研究现状及发展

我国是水果生产第一大国,水果采收后处理分级有利于销售按级定价,方便贮运;通过检测剔除虫害果和机械损伤果,减轻了病虫害的传播和降低残次品处理成本;对于提高我国果品品质,增强国际市场竞争力有着重要的意义。通过对国内分级检测的技术方法的介绍,分析了目前分级检测技术的不足,提出了相关的发展建议。     

Speech / music classification using speech-specific features

This paper proposes the use of speech-specific features for speech / music classification. Features representing the excitation source, vocal tract system and syllabic rate of speech are explored. The normalized autocorrelation peak strength of zero frequency filtered signal, and peak-to-sidelobe ratio of the Hilbert envelope of linear prediction residual are the two source features. The log mel energy feature represents the vocal tract information. The modulation spectrum represents the slowly-varying temporal envelope corresponding to the speech syllabic rate. The novelty of the present work is in analyzing the behavior of these features for the discrimination of speech and music regions. These features are non-linearly mapped and combined to perform the classification task using a threshold based approach. Further, the performance of speech-specific features is evaluated using classifiers such as Gaussian mixture models, and support vector machines. It is observed that the performance of the speech-specific features is better compared to existing features. Additional improvement for speech / music classification is achieved when speech-specific features are combined with the existing ones, indicating different aspects of information exploited by the former.     

A new fast associative classification algorithm for detecting phishing websites

Associative classification (AC) is a new, effective supervised learning approach that aims to predict unseen instances. AC effectively integrates association rule mining and classification, and produces more accurate results than other traditional data mining classification algorithms. In this paper, we propose a new AC algorithm called the Fast Associative Classification Algorithm (FACA). We investigate our proposed algorithm against four well-known AC algorithms (CBA, CMAR, MCAR, and ECAR) on real-world phishing datasets. The bases of the investigation in our experiments are classification accuracy and the F1 evaluation measures. The results indicate that FACA is very successful with regard to the F1 evaluation measure compared with the other four well-known algorithms (CBA, CMAR, MCAR, and ECAR). The FACA also outperformed the other four AC algorithms with regard to the accuracy evaluation measure.     

Microarray gene expression classification with few genes: Criteria to combine attribute selection and classification methods

Microarray data classification is a task involving high dimensionality and small samples sizes. A common criterion to decide on the number of selected genes is maximizing the accuracy, which risks overfitting and usually selects more genes than actually needed. We propose, relaxing the maximum accuracy criterion, to select the combination of attribute selection and classification algorithm that using less attributes has an accuracy not statistically significantly worst that the best. Also we give some advice to choose a suitable combination of attribute selection and classifying algorithms for a good accuracy when using a low number of gene expressions. We used some well known attribute selection methods (FCBF, ReliefF and SVM-RFE, plus a Random selection, used as a base line technique) and classifying techniques (Naive Bayes, 3 Nearest Neighbor and SVM with linear kernel) applied to 30 data sets involving different cancer types.     

An Extension Sample Classification‐Based Extreme Learning Machine Ensemble Method for Process Fault Diagnosis

In order to achieve higher accuracy and faster response in complex process fault diagnosis, an extension sample classification‐based extreme learning machine ensemble (ESC‐ELME) method is proposed. In the realization process, the extension sample classification is used to divide the fault types. For each fault type, a specific extreme learning machine (ELM) is established and trained independently. Then, all specific ELMs are integrated to determine which fault is happened by the majority voting method. The proposed ESC‐ELME method is compared with the traditional ELM and a duty‐oriented hierarchical artificial neural network in fault diagnosis of the Tennessee Eastman process. The results demonstrate that the proposed method provides higher diagnosis accuracy and faster response.     

A tribe competition-based genetic algorithm for feature selection in pattern classification

Feature selection has always been a critical step in pattern recognition, in which evolutionary algorithms, such as the genetic algorithm (GA), are most commonly used. However, the individual encoding scheme used in various GAs would either pose a bias on the solution or require a pre-specified number of features, and hence may lead to less accurate results. In this paper, a tribe competition-based genetic algorithm (TCbGA) is proposed for feature selection in pattern classification. The population of individuals is divided into multiple tribes, and the initialization and evolutionary operations are modified to ensure that the number of selected features in each tribe follows a Gaussian distribution. Thus each tribe focuses on exploring a specific part of the solution space. Meanwhile, tribe competition is introduced to the evolution process, which allows the winning tribes, which produce better individuals, to enlarge their sizes, i.e. having more individuals to search their parts of the solution space. This algorithm, therefore, avoids the bias on solutions and requirement of a pre-specified number of features. We have evaluated our algorithm against several state-of-the-art feature selection approaches on 20 benchmark datasets. Our results suggest that the proposed TCbGA algorithm can identify the optimal feature subset more effectively and produce more accurate pattern classification.