The MLPM 2014 workshop provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Machine Learning for developing predictive models for various IT related problems, targeting the provision of alternative, interdisciplinary approaches for tackling problems found in various IT disciplines. Machine Learning models are efficient for handing complex prediction models due to their outstanding performance in handling large scale datasets with uniform characteristics and noisy data. Examples of MLPM domains include health care, cyber security, education, credit card fraud detection, social media, sales forecasting, stock market forecasting, cloud computing, software measurement, quality and defect prediction, cost and effort estimation, software reuse, weather prediction, etc.

The aim of this workshop is to obtain a good perspective into the current state of practice of Machine Learning to address various predictive problems. Some topics relevant to this workshop include, but are not limited to:

Clustering and Classification
Decision Support
Support Vector Machine
Time Series
Decision Trees
Fuzzy Logic & Systems
Probabilistic Reasoning
Lazy Learning
Recommender Systems
Expert Systems
Artificial Neural Networks
Evolutionary Algorithms
Ranking Algorithms
Cognitive Processes
Evolutionary Computing
Swarm Intelligence
Artificial Immune Systems
Instance–Based Learning
Chaos Theory
Multi-Valued Logic
Ensemble Techniques
Hybrid Intelligent Models
Reasoning Models

Applied to:


Health Care Applications, Education, Cyber Security, Credit Card Fraud Detection,
Software Process and Performance, Software Cost Estimation, Software Reliability,
Software Risk Management, Software Quality and Testing, Cloud Computing, Social Media,
Business Applications,Sale Forecasting , Stock Market Forecasting, Weather Predictions
Bio-Medical Applications, Big data Applications, Intelligent Traffic Management,
Image Processing, E-Government , E-Business, E-Commerce, Networking



Researchers are encouraged to submit their contributions that have direct impact or high dependence on practical issues encountered in real-world environments. Industrial- and market-oriented research papers are also highly encouraged. To this end, MLPM's Program Committee consists of members not only from academic/research institutions but also from the industry, and these members will assist in the review process in cases of industrial and market-related papers.

**Submission & Publication:
Accepted papers will be published in IEEEXplore (IEEE Digital Library).
Registration is required for all participants. To be published in the proceedings, every accepted paper MUST have at least one registered author at the full rate and must be presented at the conference. Please visit the main website of the conference for more information.

**Special Issue
Authors of selected papers of MLPM 2014 are invited to extend their work and submit to a journal paper in a special issue “Predictive Analytics Using Machine Learning” to be appeared in Neural Computing and Applications Journal (Springer) http://goo.gl/8rDiIj.