The dataset you’ll examine is from a study by the California Department of Corrections (CDC) on the effectiveness of prisoner placement, and the likelihood of misconduct while incarcerated. Upon admission to a California prison, an inmate is given a questionnaire. The score on this questionnaire determines which level of security (Level I is the lightest security, Level IV the heaviest) to whic...

Global models of a dataset reflect not only the large scale structure of the data distribution, they also reflect small(er) scale structure. Hence, if one wants to see the large scale structure, one should somehow subtract this smaller scale structure from the model. While for some kinds of model – such as boosted classifiers – it is easy to see the “important” components, for many kind of mode...

We introduce MD sketches which are a particular kind of Finite Sum sketches Two interesting results about MD sketches are proved First we show that given two MD sketches it is algorithmically decidable whether their model categories are equivalent Next we show that data speci cations as used in database design and software engineering can be translated to MD sketches As a corollary we obtain th...

Multivariate temporal data are hybrid data. Numeric and categorical data type could be consisted of. Most past researches cannot be operated directly on the multivariate temporal data with both types. Additionally, no useful and readable rules are provided in their methods for advanced classification analysis. We proposed Progressive Temporal Class Rule Miner (PTCR-Miner) algorithm to achieve t...

We discuss a novel diagnostic method for predicting the early recurrence of liver cancer with high accuracy for personalized medicine. The difficulty with cancer treatment is that even if the types of cancer are the same, the cancers vary depending on the patient. Thus, remarkable attention has been paid to personalized medicine. Unfortunately, although the Tokyo Score, the Modified JIS, and th...

fuzzy rule-based classification system (frbcs) is a popular machine learning technique for classification purposes. one of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. however many cases the minority classes are more important than the majority ones. in this paper, we have extended ...

The performance of categorical music emotion classification that divides emotion into classes and uses audio features alone for emotion classification has reached a limit due to the presence of a semantic gap between the object feature level and the human cognitive level of emotion perception. Motivated by the fact that lyrics carry rich semantic information of a song, we propose a multi-modal ...

The classification of fuzzy uncertain data is considered one of the most challenging issues in data analysis. In spite of the significance of fuzzy data in mathematical programming, the development of the analytical methods of fuzzy data is slow. Therefore, the current study proposes a new fuzzy data classification method based on fuzzy data envelopment analysis (DEA) which can handle strea...

In this paper we present a method for learning a discriminative classifier from unlabeled or partially labeled data. Our approach is based on an objective function that trades-off mutual information between observed examples and their predicted categorical class distribution, against robustness of the classifier to an adversarial generative model. The resulting algorithm can either be interpret...