Indian Institute of Technology Palakkad&nbsp;(IIT Palakkad or IIT PKD) is a public autonomous&nbsp;engineering&nbsp;and&nbsp;research&nbsp;institute located in&nbsp;Palakkad,&nbsp;Kerala. It was&nbsp;one of the five new&nbsp;IITs&nbsp;proposed in the&nbsp;2014 Union budget of India. The campus was inaugurated on 3 August 2015. The institute has 94 faculty, 978 students, and 63 non-teaching staffs.

IT Palakkad currently offers four-year Bachelor of Technology (B.Tech) and two years MS programmes in 8 different engineering disciplines including&nbsp;Computer Science, Electrical, Mechanical, Civil among others.

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IIT Palakkad

Heterogeneity of features and lack of correspondence between data points of different domains are the two primary challenges while performing feature transfer. In this paper, we present a novel supervised domain adaptation algorithm (SHDA-RF) that learns the mapping between heterogeneous features of different dimensions. Our algorithm uses the shared label distributions present across the domains as pivots for learning a sparse feature transformation. The shared label distributions and the relationship between the feature spaces and the label distributions are estimated in a supervised manner using random forests. We conduct extensive experiments on three diverse datasets of varying dimensions and sparsity to verify the superiority of the proposed approach over other baseline and state of the art transfer approaches.

World University root node

Department of Data Science

International Joint Conference on Artificial Intelligence

Supervised Heterogeneous Domain Adaptation via Random Forests

As machine learning techniques mature and are used to tackle complex scientific problems, challenges arise such as the imbalanced class distribution problem, where one of the target class labels is under-represented in comparison with other classes. Existing oversampling approaches for addressing this problem typically do not consider the probability distribution of the minority class while synthetically generating new samples. As a result, the minority class is not represented well which leads to high misclassification error. We introduce two probabilistic oversampling approaches, namely RACOG and wRACOG, to synthetically generating and strategically selecting new minority class samples. The proposed approaches use the joint probability distribution of data attributes and Gibbs sampling to generate new minority class samples. While RACOG selects samples produced by the Gibbs sampler based on a predefined lag, wRACOG selects those samples that have the highest probability of being misclassified by the existing learning model. We validate our approach using nine UCI data sets that were carefully modified to exhibit class imbalance and one new application domain data set with inherent extreme class imbalance. In addition, we compare the classification performance of the proposed methods with three other existing resampling techniques.

IEEE Transactions on Knowledge and Data Engineering

RACOG and wRACOG: Two probabilistic oversampling techniques

Defines the notion of an activity model learned from sensor data and presents key algorithms that form the core of the field Activity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data provides an in-depth look at computational approaches to activity learning from sensor data. Each chapter is constructed to provide practical, step-by-step information on how to analyze and process sensor data. The book discusses techniques for activity learning that include the following: Discovering activity patterns that emerge from behavior-based sensor data Recognizing occurrences of predefined or discovered activities in real time Predicting the occurrences of activities The techniques covered can be applied to numerous fields, including security, telecommunications, healthcare, smart grids and home automation. An online companion site enables readers to experiment with the techniques described in the book and to adapt or enhance the techniques for their own use. With an emphasis on computational approaches, Activity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data provides graduate students and researchers with an algorithmic perspective to activity learning.

Activity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data

Activity learning: Discovering, recognizing and predicting human behavior from sensor data

Advances in pervasive and ubiquitous computing have resulted in the development of sensors that can be easily deployed in the natural habitat of a human to acquire activity related data. However, inferring meaningful activity information from sensor data is still a challenging problem. This paper addresses the problem of inferring activities that are simultaneously performed by multiple residents in a smart home or single resident performing multiple activities concurrently. The paper formulates this problem as learning multiple activity labels from a sequence of sensor data. It investigates the suitability of multi-label learning algorithms inspired by decision trees as a proposed solution to the problem. The results obtained from the experiments on four benchmarking multi-resident activity datasets clearly indicate the superiority of decision tree ensemble (random forests) based approaches for multi-label learning.