Most existing methods of human attribute recognition are part-based, where features are extracted at human body parts corresponding to each human attribute and the part-based features are then fed to classifiers individually or together for recognizing human attributes. The performance of these methods is highly dependent on the accuracy of body-part detection, which is a well known challenging...

This paper examines the Attribute Decomposition Approach with simple Bayesian combination for dealing with classification problems that contain high number of attributes and moderate numbers of records. According to the Attribute Decomposition Approach, the set of input attributes is automatically decomposed into several subsets. A classification model is built for each subset, then all the mod...

Problem statement: Predicting the value for missing attributes is an important data preprocessing problem in data mining and knowledge discovery tasks. Several methods have been proposed to treat missing data and the one used more frequently is deleting instances containing at least one missing value of a feature. When the dataset has minimum number of missing attribute values then we can negle...

We propose partitioning-based methods to facilitate the classification of 3-D binary image data sets of regions of interest (ROIs) with highly non-uniform distributions. The first method is based on recursive dynamic partitioning of a 3-D volume into a number of 3-D hyper-rectangles. For each hyper-rectangle, we consider, as a potential attribute, the number of voxels (volume elements) that bel...

Rule based classification is one of the most popular way of classification in data mining. There are number of algorithms for rule based classification. C4.5 and Partial Decision Tree (PART) are very popular algorithms among them and both have many empirical features such as continuous number categorization, missing value handling, etc. However in many cases these algorithms takes more processi...

Rough set is a valid mathematical tool that deals with uncertain, vague and incomplete information of decision systems. Attribute reduction is one of the issues in rough set theory, and core attributes are indispensable in the process of attribute reduction. Recently, some researchers proposed the method of binary discernibility matrix to compute the results of attribute reduction, which can no...

The objectives of this paper were to 1) develop an empirical method for selecting relevant attributes for modelling drought and 2) select the most relevant attribute for drought modelling and predictions in the Greater Horn of Africa (GHA). Twenty four attributes from different domain areas were used for this experimental analysis. Two attribute selection algorithms were used for the current st...

This paper proposes a new approach based on Bonferroni mean operator and possibility degree to solve fuzzy multi-attribute decision making (FMADM) problems in which the attribute value takes the form of interval type-2 fuzzy numbers. We introduce the concepts of interval possibility mean value and present a new method for calculating the possibility degree of two interval trapezoidal type-2 fuz...

Background: In this paper, a generic hesitant fuzzy set (HFS) model for clustering various ECG beats according to weights of attributes is proposed. A comprehensive review of the electrocardiogram signal classification and segmentation methodologies indicates that algorithms which are able to effectively handle the nonstationary and uncertainty of the signals should be used for ECG analysis. Ex...

In this paper, we discover and annotate visual attributes for the COCO dataset. With the goal of enabling deeper object understanding, we deliver the largest attribute dataset to date. Using our COCO Attributes dataset, a fine-tuned classification system can do more than recognize object categories – for example, rendering multi-label classifications such as “sleeping spotted curled-up cat” ins...